Recently, the Kernelized Correlation Filters tracker (KCF) achieved competitive performance and robustness in visual object tracking. On the other hand, visual trackers are not typically used in multiple object tracking. In this paper, we investigate how a robust visual tracker like KCF can improve multiple object tracking. Since KCF is a fast tracker, many can be used in parallel and still result in fast tracking. We build a multiple object tracking system based on KCF and background subtraction. Background subtraction is applied to extract moving objects and get their scale and size in combination with KCF outputs, while KCF is used for data association and to handle fragmentation and occlusion problems. As a result, KCF and background subtraction help each other to take tracking decision at every frame. Sometimes KCF outputs are the most trustworthy (e.g. during occlusion), while in some other case, it is the background subtraction outputs. To validate the effectiveness of our system, the algorithm is demonstrated on four urban video recordings from a standard dataset. Results show that our method is competitive with state-of-the-art trackers even if we use a much simpler data association step.
Jun @cite_4 used background subtraction to estimate the object properties. A watershed segmentation technique is used to over-segment the vehicles. The over-segmented patches are then merged using the common motion information of tracked feature points. This allows to segment vehicles correctly even in the presence of partial occlusion. Kim @cite_12 combines background subtraction and feature tracking approach with a multi-level clustering algorithm based on the Expectation-Maximization (EM) algorithm to handle the various object sizes in the scene. The resulting algorithm tracks various road users such as pedestrians, vehicles and cyclists online and the results can then be manually corrected in a graphical interface. Mendes @cite_13 proposed a method that also combines KLT and background subtraction. This approach increases its accuracy by setting and regions to reconfirm object trajectories and by segmenting regions when occlusions are detected.
@cite_13 @cite_4 @cite_12
2015148749 2187027887 2949192504 2161841955
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. The recent development of fast depth map fusion technique enables the realtime, detailed scene reconstruction using commodity depth camera, making the indoor scene understanding more possible than ever. To address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori. The key idea is that while contextual information could benefit the detection of either individual objects or object groups, both can contribute to object extraction when objects are unknown.Our method starts with a robust segmentation and partitions a subscene into segments, each of which represents either an independent object or a part of some object. A set of classifiers are trained for both individual objects and object groups, using a database of 3D scene models. We employ the multiple kernel learning (MKL) to learn per-category optimized classifiers for objects and object groups. Finally, we perform a graph matching to extract objects using the classifiers, thus grouping the segments into either an object or an object group. The output is an object-level labeled segmentation of the input subscene. Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes. Graphical abstractDisplay Omitted HighlightsTo address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts.our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing objects.The key idea is that while contextual information could benefit the detection of either individual objects or object groups, when object are unknown, both can contribute to object extraction. Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection. We achieve this goal by means of a multi-scale deep network that consists of five convolutional layers and a bifurcated fully-connected sub-network. The section from the input layer to the fifth convolutional layer is fixed and directly lifted from a pre-trained network optimized over a large-scale object classification task. This section of the network is applied to four different scales of the image input. These four parallel and identical streams are then attached to a bifurcated sub-network consisting of two independently-trained branches. One branch learns to predict the contour likelihood (with a classification objective) whereas the other branch is trained to learn the fraction of human labelers agreeing about the contour presence at a given point (with a regression criterion). We show that without any feature engineering our multi-scale deep learning approach achieves state-of-the-art results in contour detection. In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.