Most of the existing multiple object tracking (MOT) methods employ the tracking-by-detection framework. Among them, the min-cost network flow optimization techniques become the most popular and standard ones. In these methods, the graph structure models the MOT problem and finds the optimal flow in a connected graph of detections to encode the accurate track trajectories. However, the existing network flow is not suitable for directly online tracking, where the tracking results depend too much on the initial detections. To solve these problems, we present a fast online MOT algorithm by introducing the minimum output sum of squared error filter. The proposed method can adaptively refine the tracking targets according to the proposed rules of correcting the detection mistakes. Furthermore, we introduce an alternative targets hypotheses to reduce the dependence on detections and adaptively refine the object detection boxes. The experimental results on the MOT 2015 benchmark demonstrate that our method achieves comparable or even better results than previous approaches.

The online learning methods are popular for visual tracking because of their robust performance for most video sequences. However, the drifting problem caused by noisy updates is still a challenge for most highly adaptive online classifiers. In visual tracking, target object appearance variation, such as deformation and long-term occlusion, easily causes noisy updates. To overcome this problem, a new real-time occlusion-aware visual tracking algorithm is introduced. First, we learn a novel two-stage classifier with circulant structure with kernel, named integrated circulant structure kernels (ICSK). The first stage is applied for transition estimation and the second is used for scale estimation. The circulant structure makes our algorithm realize fast learning and detection. Then, the ICSK is used to detect the target without occlusion and build a classifier pool to save these classifiers with noisy updates. When the target is in heavy occlusion or after long-term occlusion, we redetect it using an optimal classifier selected from the classifier-pool according to an entropy minimization criterion. Extensive experimental results on the full benchmark demonstrate our real-time algorithm achieves better performance than state-of-the-art methods.