Fast and Robust Object Tracking via Probability Continuous Outlier Model

Exploiting Spatial-Temporal Locality of Tracking via Structured Dictionary Learning

In this paper, a novel spatial-temporal locality is proposed and unified via a discriminative dictionary learning framework for visual tracking. By exploring the strong local correlations between temporally obtained target and their spatially distributed nearby background neighbors, a spatial-temporal locality is obtained. The locality is formulated as a subspace model and exploited under a unified structure of discriminative dictionary learning with a subspace structure. Using the learned dictionary, the target and its background can be described and distinguished effectively through their sparse codes. As a result, the target is localized by integrating both the descriptive and the discriminative qualities. Extensive experiments on various challenging video sequences demonstrate the superior performance of proposed algorithm over the other state-of-the-art approaches.

Visual tracking via improving motion model and model updater

Motion model and model updater are two necessary components for online visual tracking. On the one hand, an effective motion model needs to strike the right balance between target processing, to account for the target appearance and scene analysis, and to describe stable background information. Most conventional trackers focus on one aspect out of the two and hence are not able to achieve the correct balance. On the other hand, the admirable model update needs to consider both the tracking speed and the model drift. Most tracking models are updated on every frame or fixed frames, so it cannot achieve the best performance. In this article, we solve the motion model problem by collaboratively using salient region detection and image segmentation. Particularly, the two methods are for different purposes. In the absence of prior knowledge, the former considers image attributes like color, gradient, edges, and boundaries then forms a robust object; the latter aggregates individual pixels into meaningful atomic regions by using the prior knowledge of target and background in the video sequence. Taking advantage of their complementary roles, we construct a more reasonable confidence map. For model update problems, we dynamically update the model by analyzing scene with image similarity, which not only reduces the update frequency of the model but also suppresses the model drift. Finally, we use these improved building blocks not only to do comparative tests but also to give a basic tracker, and extensive experimental results on OTB50 show that the proposed methods perform favorably against the state-of-the-art methods.

Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects

In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained "in one shot" at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking.

Occlusion-Aware Fragment-Based Tracking With Spatial-Temporal Consistency

In this paper, we present a robust tracking method by exploiting a fragment-based appearance model with consideration of both temporal continuity and discontinuity information. From the perspective of probability theory, the proposed tracking algorithm can be viewed as a two-stage optimization problem. In the first stage, by adopting the estimated occlusion state as a prior, the optimal state of the tracked object can be obtained by solving an optimization problem, where the objective function is designed based on the classification score, occlusion prior, and temporal continuity information. In the second stage, we propose a discriminative occlusion model, which exploits both foreground and background information to detect the possible occlusion, and also models the consistency of occlusion labels among different frames. In addition, a simple yet effective training strategy is introduced during the model training (and updating) process, with which the effects of spatial-temporal consistency are properly weighted. The proposed tracker is evaluated by using the recent benchmark data set, on which the results demonstrate that our tracker performs favorably against other state-of-the-art tracking algorithms.

Sparse Hashing Tracking

In this paper, we propose a novel tracking framework based on a sparse and discriminative hashing method. Different from the previous work, we treat object tracking as an approximate nearest neighbor searching process in a binary space. Using the hash functions, the target templates and the candidates can be projected into the Hamming space, facilitating the distance calculation and tracking efficiency. First, we integrate both the inter-class and intra-class information to train multiple hash functions for better classification, while most classifiers in previous tracking methods usually neglect the inter-class correlation, which may cause the inaccuracy. Then, we introduce sparsity into the hash coefficient vectors for dynamic feature selection, which is crucial to select the discriminative and stable features to adapt to visual variations during the tracking process. Extensive experiments on various challenging sequences show that the proposed algorithm performs favorably against the state-of-the-art methods.