Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7 in average precision score over other baseline approaches when tested on real BEV point cloud images.
On the other hand, in @cite_17 proposed deep-learning based approach for thermal infra-red object tracking. To overcome the scarcity of thermal images dataset, they utilised DA based on the CycleGAN architecture to transform images from visual domain to the thermal infra-red domain.
@cite_17
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Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74 recognition accuracy) over the high-level visual feature approach (53.16 recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased. This chapter discusses the challenges of automating surveillance and reconnaissance tasks for infra-red visual data obtained from aerial platforms. These problems have gained significant importance over the years, especially with the advent of lightweight and reliable imaging devices. Detection and tracking of objects of interest has traditionally been an area of interest in the computer vision literature. These tasks are rendered especially challenging in aerial sequences of infra red modality. The chapter gives an overview of these problems, and the associated limitations of some of the conventional techniques typically employed for these applications. We begin with a study of various image registration techniques that are required to eliminate motion induced by the motion of the aerial sensor. Next, we present a technique for detecting moving objects from the ego-motion compensated input sequence. Finally, we describe a methodology for tracking already detected objects using their motion history. We substantiate our claims with results on a wide range of aerial video sequences. In this work, we propose a new integrated framework that addresses the problems of thermal-visible video registration, sensor fusion, and people tracking for far-range videos. The video registration is based on a RANSAC trajectory-to-trajectory matching, which estimates an affine transformation matrix that maximizes the overlapping of thermal and visible foreground pixels. Sensor fusion uses the aligned images to compute sum-rule silhouettes, and then constructs thermal-visible object models. Finally, multiple object tracking uses blobs constructed in sensor fusion to output the trajectories. Results demonstrate the advantage of our proposed framework in obtaining better results for both image registration and tracking than separate image registration and tracking methods. This paper investigates how to integrate the complementary information from RGB and thermal (RGB-T) sources for object tracking. We propose a novel Convolutional Neural Network (ConvNet) architecture, including a two-stream ConvNet and a FusionNet, to achieve adaptive fusion of different source data for robust RGB-T tracking. Both RGB and thermal streams extract generic semantic information of the target object. In particular, the thermal stream is pre-trained on the ImageNet dataset to encode rich semantic information, and then fine-tuned using thermal images to capture the specific properties of thermal information. For adaptive fusion of different modalities while avoiding redundant noises, the FusionNet is employed to select most discriminative feature maps from the outputs of the two-stream ConvNet, and updated online to adapt to appearance variations of the target object. Finally, the object locations are efficiently predicted by applying the multi-channel correlation filter on the fused feature maps. Extensive experiments on the recently public benchmark GTOT verify the effectiveness of the proposed approach against other state-of-the-art RGB-T trackers. Thermal to visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or extract robust features from these modalities for cross-modal matching. In this paper, we take a different approach in which we make use of the attributes extracted from the visible image to synthesize the attribute-preserved visible image from the input thermal image for cross-modal matching. A pre-trained VGG-Face network is used to extract the attributes from the visible image. Then, a novel Attribute Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a deep network is used to extract features from the synthesized image and the input visible image for verification. Extensive experiments on the ARL Polarimetric face dataset show that the proposed method achieves significant improvements over the state-of-the-art methods. This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the significantly smaller amount of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the form of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN. ABSTRACT In low light conditions, visible light face identication is infeasible due to the lack of illumination. For nighttimesurveillance, thermal imaging is commonly used because of the intrinsic emissivity of thermal radiation from thehuman body. However, matching thermal images of faces acquired at nighttime to the predominantly visiblelight face imagery in existing government databases and watch lists is a challenging task. The diculty arisesfrom the signicant dierence between the faces thermal signature and its visible signature (i.e. the modalitygap). To match the thermal face to the visible face acquired by the two dierent modalities, we applied facerecognition algorithms that reduce the modality gap in each step of face identication, from low-level analysis tomachine learning techniques. Specically, partial least squares-discriminant analysis (PLS-DA) based approacheswere used to correlate the thermal face signatures to the visible face signatures, yielding a thermal-to-visible faceidentication rate of 49.9 . While this work makes progress for thermal-to-visible face recognition, more eortsneed to be devoted to solving this dicult task. Successful development of a thermal-to-visible face recognitionsystem would signicantly enhance the Nations nighttime surveillance capabilities.Keywords: thermal, visible, thermal-to-visible face recognition, face, recognition, multi, modal This paper studies the problem of object tracking in challenging scenarios by leveraging multimodal visual data. We propose a grayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation. Given one bounding box, we extract a set of overlapping local patches within it, and pursue the multitask joint sparse representation for grayscale and thermal modalities. Then, the representation coefficients of the two modalities are concatenated into a vector to represent the feature of the bounding box. Moreover, the similarity between each patch pair is deployed to refine their representation coefficients in the sparse representation, which can be formulated as the Laplacian sparse representation. We also incorporate the modal reliability into the Laplacian sparse representation to achieve an adaptive fusion of different source data. Experiments on two grayscale-thermal datasets suggest that the proposed approach outperforms both grayscale and grayscale-thermal tracking approaches. In this paper, we propose a novel graph model, called weighted sparse representation regularized graph, to learn a robust object representation using multispectral (RGB and thermal) data for visual tracking. In particular, the tracked object is represented with a graph with image patches as nodes. This graph is dynamically learned from two aspects. First, the graph affinity (i.e., graph structure and edge weights) that indicates the appearance compatibility of two neighboring nodes is optimized based on the weighted sparse representation, in which the modality weight is introduced to leverage RGB and thermal information adaptively. Second, each node weight that indicates how likely it belongs to the foreground is propagated from others along with graph affinity. The optimized patch weights are then imposed on the extracted RGB and thermal features, and the target object is finally located by adopting the structured SVM algorithm. Moreover, we also contribute a comprehensive dataset for RGB-T tracking purpose. Comparing with existing ones, the new dataset has the following advantages: 1) Its size is sufficiently large for large-scale performance evaluation (total frame number: 210K, maximum frames per video pair: 8K). 2) The alignment between RGB-T video pairs is highly accurate, which does not need pre- and post-processing. 3) The occlusion levels are annotated for analyzing the occlusion-sensitive performance of different methods. Extensive experiments on both public and newly created datasets demonstrate the effectiveness of the proposed tracker against several state-of-the-art tracking methods. Abstract. Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks. Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking. We present a new background-subtraction technique fusing contours from thermal and visible imagery for persistent object detection in urban settings. Statistical background-subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then fused into a single image. An A^* path-constrained search along watershed boundaries of the regions-of-interest is used to complete and close any broken segments in the fused contour image. Lastly, the contour image is flood-filled to produce silhouettes. Results of our approach are evaluated quantitatively and compared with other low- and high-level fusion techniques using manually segmented data. Face recognition research has primarily focused on the visible spectrum, due to the prevalence and low cost of visible cameras. However, face recognition in the visible spectrum is sensitive to illumination variations, and is infeasible in low-light or nighttime settings. In contrast, thermal imaging acquires naturally emitted radiation from facial skin tissue, and is therefore ideal for nighttime surveillance and intelligence gathering operations. However, conventional thermal face imagery lacks textural and geometrics details that are present in visible spectrum face signatures. In this work, we further explore the impact of polarimetric imaging in the LWIR spectrum for face recognition. Polarization-state information provides textural and geometric facial details unavailable with conventional thermal imaging. Since the frequency content of the conventional thermal, polarimetric thermal, and visible images is quite different, we propose a spatial correlation based procedure to optimize the filtering of polarimetric thermal and visible face images to further facilitate cross-spectrum face recognition. Additionally, we use a more extensive gallery database to more robustly demonstrate an improvement in the performance of cross-spectrum face recognition using polarimetric thermal imaging. High-performance microprocessor families employ dynamic-thermal-management techniques to cope with the increasing thermal stress resulting from peaking power densities. These techniques operate on feedback generated from on-die thermal sensors. The allocation and the placement of thermal-sensing elements directly impact the effectiveness of the dynamic management mechanisms. In this paper, we propose systematic techniques for determining the optimal locations for thermal sensors to provide high-fidelity thermal monitoring of a complex microprocessor system. Our strategies can be divided into two main categories: uniform sensor allocation and nonuniform sensor allocation. In the uniform approach, the sensors are placed on a regular grid. The nonuniform allocation identifies an optimal physical location for each sensor such that the sensor's attraction toward steep thermal gradients is maximized, which can result in uneven concentrations of sensors on different locations of the chip. We also present a hybrid algorithm that shows the tradeoffs associated with number of sensors and expected accuracy. Our experimental results show that our uniform approach using interpolation can detect the chip temperature with a maximum error of 5.47degC and an average maximum error of 1.05degC . On the other hand, our nonuniform strategy is able to create a sensor distribution for a given microprocessor architecture, providing thermal measurements with a maximum error of 3.18degC and an average maximum error of 1.63degC across a wide set of applications. We propose a human body tracking method using a far-infrared sensor array. A far-infrared sensor array captures the spatial distribution of temperature as a low-resolution image. Since it is difficult to identify a person from the low-resolution thermal image, we can avoid privacy issues. Therefore, it is expected to be applied for the analysis of human behaviors in various places. However, it is difficult to accurately track humans because of the lack of information sufficient to describe the feature of the target human body in the low-resolution thermal image. In order to solve this problem, we propose a thermo-spatial sensitive histogram suitable to represent the target in the low-resolution thermal image. Unlike the conventional histograms, in case of the thermo-spatial sensitive histogram, a voting value is weighted depending on the distance to the target’s position and the difference from the target’s temperature. This histogram allows the accurate tracking by representing the target with multiple histograms and reducing the influence of the background pixels. Based on this histogram, the proposed method tracks humans robustly to occlusions, pose variations, and background clutters. We demonstrate the effectiveness of the method through an experiment using various image sequences. We present a series of long-wave-infrared (LWIR) polarimetric-based thermal images of facial profiles in which polarization-state information of the image-forming radiance is retained and displayed. The resultant polarimetric images show enhanced facial features, additional texture, and details that are not present in corresponding conventional thermal imagery. It has been generally thought that conventional thermal imagery (MidIR or LWIR) could not produce the detailed spatial information required for reliable human identification due to the so-called “ghosting” effect often seen in thermal imagery of human subjects. By using polarimetric information, we are able to extract subtle surface features of the human face, thus improving subject identification. Polarimetric image sets considered include the conventional thermal intensity image, S0, the two Stokes images, S1 and S2, and a Stokes image product called the degree-of-linear-polarization image. The increased power densities of multi-core processors and the variations within and across workloads lead to runtime thermal hot spots locations of which change across time and space. Thermal hot spots increase leakage, deteriorate timing, and reduce the mean time to failure. To manage runtime thermal variations, circuit designers embed within-die thermal sensors that acquire temperatures at few selected locations. The acquired temperatures are then used to guide runtime thermal management techniques. The capabilities of these techniques are essentially bounded by the spatial thermal resolution of the sensor measurements. In this paper we characterize temperature signals of real processors and demonstrate that on-chip thermal gradients lead to sparse signals in the frequency domain. We exploit this observation to (1) devise thermal sensor allocation techniques, and (2) devise signal reconstruction techniques that fully characterize the thermal status of the processor using the limited number of measurements from the thermal sensors. To verify the accuracy of our methods, we compare our temperature characterization results against thermal measurements acquired from a state-of-the-art infrared camera that captures the mid-band infrared emissions from the back of the die of a 45 nm dual-core processor. Our results show that our techniques are capable of accurately characterizing the temperatures of real processors. We present an interactive material acquisition system for average users to capture the spatially varying appearance of daily objects. While an object is being scanned, our system estimates its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect sensor, a mirror ball and printed markers. We exploit the Kinect infra-red emitter receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. PINs and patterns remain among the most widely used knowledge-based authentication schemes. As thermal cameras become ubiquitous and affordable, we foresee a new form of threat to user privacy on mobile devices. Thermal cameras allow performing thermal attacks, where heat traces, resulting from authentication, can be used to reconstruct passwords. In this work we investigate in details the viability of exploiting thermal imaging to infer PINs and patterns on mobile devices. We present a study (N=18) where we evaluated how properties of PINs and patterns influence their thermal attacks resistance. We found that thermal attacks are indeed viable on mobile devices; overlapping patterns significantly decrease successful thermal attack rate from 100 to 16.67 , while PINs remain vulnerable (>72 success rate) even with duplicate digits. We conclude by recommendations for users and designers of authentication schemes on how to resist thermal attacks. We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.