The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods.

Vision‐based traffic accident detection is one of the challenging tasks in intelligent transportation systems due to the multi‐modalities of traffic accidents. The first challenging issue is about how to learn robust and discriminative spatio‐temporal feature representations. Since few training samples of traffic accidents can be collected, sparse coding techniques can be used for small data case. However, most sparse coding algorithms which use norm regularisation may not achieve enough sparsity. The second challenging issue is about the sample imbalance between traffic accidents and normal traffic such that detector would like to favour normal traffic. This study proposes a traffic accident detection method, including a self‐tuning iterative hard thresholding (ST‐IHT) algorithm for learning sparse spatio‐temporal features and a weighted extreme learning machine (W‐ELM) for detection. The ST‐IHT algorithm can improve the sparsity of encoded features by solving an norm regularisation. The W‐ELM can put more focus on traffic accident samples. Meanwhile, a two‐point search strategy is proposed to adaptively find a candidate value of Lipschitz coefficients to improve the tuning precision. Experimental results in our collected dataset have shown that this proposed traffic accident detection algorithm outperforms other state‐of‐the‐art methods in terms of the feature's sparsity and detection performance.

In this paper, a structured L _2,1 optimization model, which simultaneously characterizes the reconstruction capability and diversity, is proposed to provide a semantically meaningful representation of a short video clip acquired from digital cameras or a mobile robot. In this model, a mutual inhabitation penalty term is imposed to prevent similar samples from being selected simultaneously. The proposed model is highly flexible to incorporate different mutual inhabitation terms and the temporal redundancy in video is exploited to encourage the diversity. The constructed objective function is nonconvex and an iterative algorithm is developed to solve the optimization problem. The performance is evaluated using various video clips from YouTube and also based on practical video captured by an indoor mobile robot. The results clearly indicate that the proposed strategy helps the optimization model to achieve more diversified key frames than the other existing work method.