The invention discloses a tracking-learning-detection (TLD)-based video object tracking method, which comprises the following steps of: (1) when a start frame of an object is tracked, generating an image sub-window according to given position and size information, and training a detector; (2) estimating the area of the object in a current frame according to the position and size information of the object in a previous frame by using a tracker; (3) detecting the current frame by using the detector, and finding all possible objects in the current frame; and (4) performing fusion processing on results of the tracker and the detector, judging whether the object exists in the current frame or not, returning to the step (3) to process the next frame if the object does not exist in the current frame, judging whether a tracking trajectory of the current frame is effective or not if the object exists in the current frame, giving the position of the object, returning to the step (2) to process the next frame if the tracking trajectory of the current frame is ineffective, finishing the online updating of the detector if the tracking trajectory of the current frame is effective, and returning to the step (2) to process the next frame. According to the method, the conventional TLD algorithm is improved to obtain a video object tracking algorithm which is more ideal than the TLD algorithm.

Tracking-learning-detection (TLD)-based video object tracking method

In computer vision, the object tracking technique is a complex task to predict the object target under challenging task occlusion, scaling, and illumination. A robust object tracking algorithm is proposed based on adaptive patch feature matching and scale estimation. Initially, tracking technique extracts geometric locations using defined directional distances and object location of the previous frame. The feature matching is carried out between the previous frame featured vector and overlapping patches feature vectors of the present frame. The matched patch is bounded by the identified location in the current frame. For object scaling, speeded up robust features point matching technique is applied to an enlarged patch of the present frame and the tracked patch of the previous frame. Finally, the proposed algorithm updates motion, feature, and geometric location vectors to keep tracking between successive frames. The feature vector update between successive frames solves the aforementioned challenging issues in object tracking. For experimentation, the proposed algorithm is tested with available tracking benchmarks. The performance evaluation results show that the proposed technique is better in terms of computational time and accuracy compared to conventional tracking techniques.

Adaptive patch feature matching and scale estimation for visual object tracking

A Two-phase Tracking Method based on Structural Entropy Appearance Model

The use of multiple features for tracking has been proved as an effective approach, for the reason that limitation of each feature could be compensated. Sparse representation with a particle filter is one of the most influential frameworks for visual tracking. Many sparse representation tracking algorithms consider the holistic or local representation of the target, and rarely take into account distribution of sparse coefficient. In this paper, we propose a group sparse representation model using multiple features for object tracking. By appropriately selecting features to form the dictionary, the group sparse based representation scheme can assign proper weights to each group of selected templates. As a result, the potential target in each frame can be identified precisely. Experimental results on public available videos demonstrate that the proposed method performs favorably against several state-of-the-art tracking methods.

Visual object tracking via multi-view and group sparse representation

Numerous trackers have been proposed in recent years with considerable success. But few trackers can cope with all scenarios without failures. It is very difficult to design a tracker robust enough to keep off tracking failure. As failure is inevitable, we propose a framework to correct tracker, verify failure, predict object position and re-detect object. The original model of the first frame is used to correct the tracker. Then, the confidence of tracking is used to verify tracking failure. Finally, the spatial context is used to predict the object location when the object is invisible and re-detect object after tracking failure. A variety of comparative experiments validate the superiority of our tracker in comparison against other trackers.

Recovery from tracking failure

Visual tracking has attracted much research due to its importance in many practical applications such as video surveillance, vehicle navigation and augmented reality. Although existing trackers have made some success under various scenarios, long-term robust tracking in real-world scenes remains an open problem. Trackers are easily drifting away without external re-initialization due to drastic deformation, heavy occlusion and complex environments. We propose a framework to correct tracker by memorizing diverse states of the object. A memory pool of object state is constructed first, then maximum posterior probability is employed to estimate whether the object is present in the view and a best fit model is picked out from the pool for current frame. The qualitative and quantitative experimental results on various long video sequences demonstrate the superior performance of our method in comparison with other long-term trackers.

Correcting the tracker with memories

Visual tracking is a significant but challenging field in computer vision. Long-term robust tracking in unconstrained environments is insurmountable due to arbitrary deformation, occlusion and background clutters. In this paper, we propose a framework to integrate couplers and historical models into a discriminative classifier for robust long-term tracking. Model updating inevitably introduces errors into the tracker, and the appearance model cannot match the object well. A best fitted model is retrieved from historical models to correct the tracker. Couplers, which are key points around the object that tightly coupled with the target, are detected to predict the position of the target when the tracker is fully occluded or distracted by other similar objects. Both quantitative and qualitative experimental results validate the superiority of our tracker over other state-of-the-art methods.

Tracking with the support of couplers and historical models

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Visual object tracking via multi-view and group sparse representation

Recovery from tracking failure

Correcting the tracker with memories

Tracking with the support of couplers and historical models