This article addresses the navigation problem of small and medium-sized unmanned aerial vehicles (UAVs) built to perform missions in forest environments (e.g., search and rescue) by exploiting the presence of natural or man-made paths, typically present in this type of environments. The proposed system extends a previous monocular-based technique for trail detection and tracking so as to take into account volumetric data acquired from a Visual SLAM algorithm and, as a result, to increase its sturdiness upon challenging trails. The experimental results, obtained via a set of 12 videos recorded with a camera installed in a tele-operated, unmanned small-sized aerial vehicle, show the ability of the proposed system to overcome some of the difficulties of the original detector, attaining a success rate of 97.8 .
To overcome the previous work's limitations, @cite_9 proposed the use of local appearance contrast for path detection. Later, @cite_30 exploited the related concept of visual saliency to detect paths. Visual saliency was exploited with a swarm-based solution, inspired by the social insects metaphor @cite_1 , capable of interpreting the input images without the cost of explicitly over-segmenting the input image and the brittleness of depending on accurate appearance and shape models. As mentioned before, this method has shown to not exhibit sufficient robustness in the presence of strong distractors in the environment. This paper proposes an extension to the this previous method, based on 3-D data acquired from a visual SLAM technique, to reduce the chances of the method to be mistakenly attracted by distractors in the environment.
@cite_30 @cite_9 @cite_1
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This paper addresses the problem of detecting salient areas within natural images. We shall mainly study the problem under unsupervised setting, i.e., saliency detection without learning from labeled images. A solution of multitask sparsity pursuit is proposed to integrate multiple types of features for detecting saliency collaboratively. Given an image described by multiple features, its saliency map is inferred by seeking the consistently sparse elements from the joint decompositions of multiple-feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a constrained nuclear norm and as an l2,1 -norm minimization problem, which is convex and can be solved efficiently with an augmented Lagrange multiplier method. Compared with previous methods, which usually make use of multiple features by combining the saliency maps obtained from individual features, the proposed method seamlessly integrates multiple features to produce jointly the saliency map with a single inference step and thus produces more accurate and reliable results. In addition to the unsupervised setting, the proposed method can be also generalized to incorporate the top-down priors obtained from supervised environment. Extensive experiments well validate its superiority over other state-of-the-art methods. This paper extends an existing saliency-based model for path detection and tracking so that the appearance of the path being followed can be learned and used to bias the saliency computation process. The goal is to reduce ambiguities in the presence of strong distractors. In both original and extended path detectors, neural and swarm models are layered in order to attain a hybrid solution. With generalisation to other tasks in mind, these detectors are presented as instances of a generic neural-swarm layered architecture for visual saliency computation. The architecture considers a swarm-based substrate for the extraction of high-level perceptual representations, given the low-level perceptual representations extracted by a neural-based substrate. The goal of this division of labour is to ensure parallelism across the vision system while maintaining scalability and tractability. The proposed model is shown to exhibit, at 20Hz, a 98.67 success rate on a diverse data-set composed of 39 videos encompassing a total of 29,789 640x480 frames. An open source implementation of the model, fully encapsulated as a node of the Robotics Operating System (ROS), is available for download. Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate objectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate objectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. We propose an object-level salient detection algorithm which explicitly explores bottom-up visual attention and objectness cues.Some category-independent object candidates are firstly segmented from the image by the quantized color attributes of images.Global cues and candidate objectness are developed, to evaluate bottom-up visual attention of the whole image and the objectness of the object candidates respectively.By explicitly combining local objectness cue with global low-level saliency cues with candidates location and color attributes, our proposed method is more suitable for processing images with complex background. A spatiotemporal saliency algorithm based on a center-surround framework is proposed. The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery. Under this formulation, the saliency of a location is equated to the power of a predefined set of features to discriminate between the visual stimuli in a center and a surround window, centered at that location. The features are spatiotemporal video patches and are modeled as dynamic textures, to achieve a principled joint characterization of the spatial and temporal components of saliency. The combination of discriminant center-surround saliency with the modeling power of dynamic textures yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras. The related problem of background subtraction is treated as the complement of saliency detection, by classifying nonsalient (with respect to appearance and motion dynamics) points in the visual field as background. The algorithm is tested for background subtraction on challenging sequences, and shown to substantially outperform various state-of-the-art techniques. Quantitatively, its average error rate is almost half that of the closest competitor. The goal of saliency detection is to locate important pixels or regions in an image which attract humans' visual attention the most. This is a fundamental task whose output may serve as the basis for further computer vision tasks like segmentation, resizing, tracking and so forth. In this paper we propose a novel salient region detection algorithm by integrating three important visual cues namely uniqueness, focus ness and objectness (UFO). In particular, uniqueness captures the appearance-derived visual contrast, focus ness reflects the fact that salient regions are often photographed in focus, and objectness helps keep completeness of detected salient regions. While uniqueness has been used for saliency detection for long, it is new to integrate focus ness and objectness for this purpose. In fact, focus ness and objectness both provide important saliency information complementary of uniqueness. In our experiments using public benchmark datasets, we show that, even with a simple pixel level combination of the three components, the proposed approach yields significant improvement compared with previously reported methods. In this paper, we present a novel salient object detection method, efficiently combining Laplacian sparse subspace clustering (LSSC) and unified low-rank representation (ULRR). Unlike traditional low-rank matrix recovery (LRMR) based saliency detection methods which mainly extract saliency from pixels or super-pixels, our method advocates the saliency detection on the super-pixel clusters generated by LSSC. By doing so, our method succeeds in extracting large-size salient objects from cluttered backgrounds, against the detection of small-size salient objects from simple backgrounds obtained by most existing work. The entire algorithm is carried out in two stages: region clustering and cluster saliency detection. In the first stage, the input image is segmented into many super-pixels, and on top of it, they are further grouped into different clusters by using LSSC. Each cluster contains multiple super-pixels having similar features (e.g., colors and intensities), and may correspond to a part of a salient object in the foreground or a local region in the background. In the second stage, we formulate the saliency detection of each super-pixel cluster as a unified low-rankness and sparsity pursuit problem using a ULRR model, which integrates a Laplacian regularization term with respect to the sparse error matrix into the traditional low-rank representation (LRR) model. The whole model is based on a sensible cluster-consistency assumption that the spatially adjacent super-pixels within the same cluster should have similar saliency values, similar representation coefficients as well as similar reconstruction errors. In addition, we construct a primitive dictionary for the ULRR model in terms of the local-global color contrast of each super-pixel. On top of it, a global saliency measure covering the representation coefficients and a local saliency measure considering the sparse reconstruction errors are jointly employed to define the final saliency measure. Comprehensive experiments over diverse publicly available benchmark data sets demonstrate the validity of the proposed method. In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts. Generic object level saliency detection is important for many vision tasks. Previous approaches are mostly built on the prior that "appearance contrast between objects and backgrounds is high". Although various computational models have been developed, the problem remains challenging and huge behavioral discrepancies between previous approaches can be observed. This suggest that the problem may still be highly ill-posed by using this prior only. In this work, we tackle the problem from a different viewpoint: we focus more on the background instead of the object. We exploit two common priors about backgrounds in natural images, namely boundary and connectivity priors, to provide more clues for the problem. Accordingly, we propose a novel saliency measure called geodesic saliency. It is intuitive, easy to interpret and allows fast implementation. Furthermore, it is complementary to previous approaches, because it benefits more from background priors while previous approaches do not. Evaluation on two databases validates that geodesic saliency achieves superior results and outperforms previous approaches by a large margin, in both accuracy and speed (2 ms per image). This illustrates that appropriate prior exploitation is helpful for the ill-posed saliency detection problem. We propose a visual SLAM (Simultaneous Localization And Mapping) system able to perform robustly in populated environments. The image stream from a moving RGB-D camera is the only input to the system. The computed map in real-time is composed of two layers: 1) The unpopulated geometrical layer, which describes the geometry of the bare scene as an occupancy grid where pieces of information corresponding to people have been removed; 2) A semantic human activity layer, which describes the trajectory of each person with respect to the unpopulated map, labelling an area as "traversable" or "occupied". Our proposal is to embed a real-time human tracker into the system. The purpose is twofold. First, to mask out of the rigid SLAM pipeline the image regions occupied by people, which boosts the robustness, the relocation, the accuracy and the reusability of the geometrical map in populated scenes. Secondly, to estimate the full trajectory of each detected person with respect to the scene map, irrespective of the location of the moving camera when the person was imaged. The proposal is tested with two popular visual SLAM systems, C2TAM and ORBSLAM2, proving its generality. The experiments process a benchmark of RGB-D sequences from camera onboard a mobile robot. They prove the robustness, accuracy and reuse capabilities of the two layer map for populated scenes. In this paper, a novel method for the detection of multiple salient regions that is based on the integration of estimated foreground clues is proposed. Although this subject has been very well studied for the detection of salient objects, many technical challenges still exist regarding the multiple-object-detection task; in particular, unlike a single-object-detection problem, a high inter-object dissimilarity causes new difficulties. By analyzing the limitations of the existing models, the following two main frameworks that are based on a multi-level foreground-segmentation strategy are proposed: non-parametric cluster-based saliency (NS) and parametric cluster-based saliency (PS). Each framework consists of a vector classification, a foreground estimation, an energy generation, and an integration process. In contrast to previous models, the proposed method is not dependent upon the contrast features, and is unaffected by the size, thickness, and shape of the objects. In the experiment results, a superior detection accuracy for the SED2 benchmark was achieved with the use of the proposed scheme; furthermore, the corresponding precision and recall are superior to those of the state-of-the-art approaches, and more effective performances were also achieved on the MSRA-ASD, SED2 and CSSD benchmarks. Display Omitted We address the problem of multiple salient region detection.The method consists of the parametric and non-parametric cluster based streams.The limitations of the existing models that are based on contrast are addressed.A spatial objectness is only considered for the computation of saliency scores. Visual tracking in unconstrained environments is very challenging due to the existence of several sources of varieties such as changes in appearance, varying lighting conditions, cluttered background, and frame-cuts. A major factor causing tracking failure is the emergence of regions having similar appearance as the target. It is even more challenging when the target leaves the field of view (FoV) leading the tracker to follow another similar object, and not reacquire the right target when it reappears. This paper presents a method to address this problem by exploiting the context on-the-fly in two terms: Distracters and Supporters. Both of them are automatically explored using a sequential randomized forest, an online template-based appearance model, and local features. Distracters are regions which have similar appearance as the target and consistently co-occur with high confidence score. The tracker must keep tracking these distracters to avoid drifting. Supporters, on the other hand, are local key-points around the target with consistent co-occurrence and motion correlation in a short time span. They play an important role in verifying the genuine target. Extensive experiments on challenging real-world video sequences show the tracking improvement when using this context information. Comparisons with several state-of-the-art approaches are also provided. Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information. Visual saliency, which predicts regions in the field of view that draw the most visual attention, has attracted a lot of interest from researchers. It has already been used in several vision tasks, e.g., image classification, object detection, foreground segmentation. Recently, the spectrum analysis based visual saliency approach has attracted a lot of interest due to its simplicity and good performance, where the phase information of the image is used to construct the saliency map. In this paper, we propose a new approach for detecting spatiotemporal visual saliency based on the phase spectrum of the videos, which is easy to implement and computationally efficient. With the proposed algorithm, we also study how the spatiotemporal saliency can be used in two important vision task, abnormality detection and spatiotemporal interest point detection. The proposed algorithm is evaluated on several commonly used datasets with comparison to the state-of-art methods from the literature. The experiments demonstrate the effectiveness of the proposed approach to spatiotemporal visual saliency detection and its application to the above vision tasks In this paper we propose a novel computational method to infer visual saliency in images. The method is based on the idea that salient objects should have local characteristics that are different than the rest of the scene, being edges, color or shape. By using a novel operator, these characteristics are combined to infer global information. The obtained information is used as a weighting for the output of a segmentation algorithm so that the salient object in the scene can easily be distinguished from the background. The proposed approach is fast and it does not require any learning. The experimentation shows that the system can enhance interesting objects in images and it is able to correctly locate the same object annotated by humans with an F-measure of 85.61 when the object size is known, and 79.19 when the object size is unknown, improving the state of the art performance on a public dataset. A bottom-up visual saliency detector is proposed, following a decision-theoretic formulation of saliency, previously developed for top-down processing (object recognition) [5]. The saliency of a given location of the visual field is defined as the power of a Gabor-like feature set to discriminate between the visual appearance of 1) a neighborhood centered at that location (the center) and 2) a neighborhood that surrounds it (the surround). Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images, so as to achieve a computationally efficient solution. The resulting saliency detector is shown to replicate the fundamental properties of the psychophysics of pre-attentive vision, including stimulus pop-out, inability to detect feature conjunctions, asymmetries with respect to feature presence vs. absence, and compliance with Weber's law. It is also shown that the detector produces better predictions of human eye fixations than two previously proposed bottom-up saliency detectors. Researchers have been taking advantage of visual attention in various image processing applications such as image retargeting, video coding, etc. Recently, many saliency detection algorithms have been proposed by extracting features in spatial or transform domains. In this paper, a novel saliency detection model is introduced by utilizing low-level features obtained from the wavelet transform domain. Firstly, wavelet transform is employed to create the multi-scale feature maps which can represent different features from edge to texture. Then, we propose a computational model for the saliency map from these features. The proposed model aims to modulate local contrast at a location with its global saliency computed based on the likelihood of the features, and the proposed model considers local center-surround differences and global contrast in the final saliency map. Experimental evaluation depicts the promising results from the proposed model by outperforming the relevant state of the art saliency detection models. We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention. Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100 precision with recall rates of up to 60 . Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we tackle the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. Our approach can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems. With the goal of effectively identifying common and salient objects in a group of relevant images, co-saliency detection has become essential for many applications such as video foreground extraction, surveillance, image retrieval, and image annotation. In this paper, we propose a unified co-saliency detection framework by introducing two novel insights: 1) looking deep to transfer higher-level representations by using the convolutional neural network with additional adaptive layers could better reflect the properties of the co-salient objects, especially their consistency among the image group; 2) looking wide to take advantage of the visually similar neighbors beyond a certain image group could effectively suppress the influence of the common background regions when formulating the intra-group consistency. In the proposed framework, the wide and deep information are explored for the object proposal windows extracted in each image, and the co-saliency scores are calculated by integrating the intra-image contrast and intra-group consistency via a principled Bayesian formulation. Finally the window-level co-saliency scores are converted to the superpixel-level co-saliency maps through a foreground region agreement strategy. Comprehensive experiments on two benchmark datasets have demonstrated the consistent performance gain of the proposed approach.