Abstract RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data. However, RGB-T research is limited by lacking a comprehensive evaluation platform. In this paper, we propose a large-scale video benchmark dataset for RGB-T tracking. It has three major advantages over existing ones: 1) Its size is sufficiently large for large-scale performance evaluation (total number of frames: 234K, maximum number of frames per sequence: 8K). 2) The alignment between RGB-T sequence pairs is highly accurate, which does not need pre- or post-processing. 3) The occlusion levels are annotated for occlusion-sensitive performance analysis of different tracking algorithms. Moreover, we propose a novel graph-based approach to learn a robust object representation for RGB-T tracking. In particular, the tracked object is represented with a graph with image patches as nodes. Given initial weights of nodes, this graph including graph structure, node weights and edge weights is dynamically learned in a unified optimization framework. Extensive experiments on the large-scale dataset are executed to demonstrate the effectiveness of the proposed tracker against other state-of-the-art tracking methods. We also provide new insights and potential research directions to the field of RGB-T object tracking.
There are several popular RGB-T video datasets. OSU-CT dataset @cite_56 contains 6 RGB-T video sequence pairs recorded from two different locations with only people moving, which is obviously not sufficient to evaluate tracking algorithms. Other two RGB-T datasets are collected by @cite_57 @cite_17 . Most of them suffer from limited size, low diversity, and high bias. Li @cite_7 construct a large size RGB-T dataset called RGBT210 with 210 RGB-T pairs. They record videos under a moving platform which enriches much diversity of the dataset, and annotate the challenging attributes for comprehensive evaluation. This dataset, however, has two issues: 1) It lacks the videos captured in hot days, and is thus highly biasd to environmental temperature since thermal sensors are sensitive to temperature. 2) Videos of two modalities share same bounding box annotations, which sometimes are unreasonable due to existence of alignment errors.
@cite_57 @cite_17 @cite_7 @cite_56
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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. Abstract When dealing with the registration of information from different image sources, the de facto similarity measure used is Mutual Information (MI). Although MI gives good performance in many image registration applications, recent works in thermal–visible registration have shown that other similarity measures can give results that are as accurate, if not more than MI. Furthermore, some of these measures also have the advantage of being calculated independently from each image to register, which allows them to be integrated more easily in energy minimization frameworks. In this article, we investigate the accuracy of similarity measures for thermal–visible image registration of human silhouettes, including MI, Sum of Squared Differences (SSD), Normalized Cross-Correlation (NCC), Histograms of Oriented Gradients (HOG), Local Self-Similarity (LSS), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Census, Fast Retina Keypoint (FREAK), and Binary Robust Independent Elementary Feature (BRIEF). We tested the various similarity measures in dense stereo matching tasks over 25,000 windows to have statistically significant results. To do so, we created a new dataset in which one to five humans are walking in a scene in various depth planes. Results show that even if MI is a very strong performer, particularly for large regions of interest (ROI), LSS gives better accuracies when ROI are small or segmented into small fragments because of its ability to capture shape. The other tested similarity measures did not give consistently accurate results. 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. 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.