In order to track moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g., 15 frames. We initially calculate a foreground map at each frame obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching. The task can be formulated by maximizing a posteriori under the Bayesian framework, in which we integrate the spatio-temporal contexts and the appearance models. The probabilistic inference is achieved by a data-driven Markov chain Monte Carlo algorithm. Given a period of observed frames, the algorithm simulates an ergodic and aperiodic Markov chain, and it visits a sequence of solution states in the joint space of spatial graph partitioning and temporal graph matching. In the experiments, our method is tested on several challenging videos from the public datasets of visual surveillance, and it outperforms the state-of-the-art methods.
(I) Sequential inference based methods use the information of the currently observed frame to predict the states of moving targets and assign their target identities. The classical examples are particle filtering @cite_41 @cite_0 @cite_28 and optical flow @cite_25 . Recently, Avidan @cite_12 proposed a learning-based tracker using the online Adaboost algorithm, which maintains a discriminative detector to track targets in the current frame. @cite_36 significantly improved the tracking performance using Multiple Instance Learning (MIL). Despite great success, these approaches may yield identity lossing (or switching) and trajectory fragmentation in terms of mutual-interaction, occlusion and spurious motion, because they make online decisions while discarding global information.
@cite_41 @cite_28 @cite_36 @cite_0 @cite_25 @cite_12
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Most tracking-by-detection algorithms train discriminative classifiers to separate target objects from their surrounding background. In this setting, noisy samples are likely to be included when they are not properly sampled, thereby causing visual drift. The multiple instance learning (MIL) paradigm has been recently applied to alleviate this problem. However, important prior information of instance labels and the most correct positive instance (i.e., the tracking result in the current frame) can be exploited using a novel formulation much simpler than an MIL approach. In this paper, we show that integrating such prior information into a supervised learning algorithm can handle visual drift more effectively and efficiently than the existing MIL tracker. We present an online discriminative feature selection algorithm that optimizes the objective function in the steepest ascent direction with respect to the positive samples while in the steepest descent direction with respect to the negative ones. Therefore, the trained classifier directly couples its score with the importance of samples, leading to a more robust and efficient tracker. Numerous experimental evaluations with state-of-the-art algorithms on challenging sequences demonstrate the merits of the proposed algorithm. The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system. This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method. We propose a biologically inspired framework for visual tracking based on discriminant center surround saliency. At each frame, discrimination of the target from the background is posed as a binary classification problem. From a pool of feature descriptors for the target and background, a subset that is most informative for classification between the two is selected using the principle of maximum marginal diversity. Using these features, the location of the target in the next frame is identified using top-down saliency, completing one iteration of the tracking algorithm. We also show that a simple extension of the framework to include motion features in a bottom-up saliency mode can robustly identify salient moving objects and automatically initialize the tracker. The connections of the proposed method to existing works on discriminant tracking are discussed. Experimental results comparing the proposed method to the state of the art in tracking are presented, showing improved performance.