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Sparse representation-based methods have been successfully applied to visual tracking. However, complex and inefficient optimization limits their deployment in practical tracking scenarios. In this paper, we propose a temporal consistency dictionary learning tracking algorithm to enable efficient dictionary learning and tracking executive. First, we present an objective function which introduces the fixed dictionary and variance dictionary to reconstruct the object’s appearance. In particular, the proposed method takes the temporal consistency into account by adding a regularization term into the objective function to constrain the difference of object appearance at adjacent frames. Then the optimization problem is solved in an iteration way. Moreover, the proposed method can encode the object’s local structural information, and the local patches from the same candidate altogether for a global appearance representation. Second, we develop an effective observation likelihood function based on the proposed model. It takes the influence of patches with large reconstruction errors into consideration, thereby, alleviating the drifting of the object. Finally, we present an appearance updating strategy to adapt to the object’s appearance variations by the online dictionary learning. Experimental evaluations on the TB50 and TB100 datasets show that the proposed tracking method outperforms sparse representation related visual tracking as well as other state-of-the-art tracking methods.

Tracking generic human motion is highly challenging due to its high-dimensional state space and the various motion types involved. In order to deal with these challenges, a fusion formulation which integrates low- and high-dimensional tracking approaches into one framework is proposed. The low-dimensional approach successfully overcomes the high-dimensional problem of tracking the motions with available training data by learning motion models, but it only works with specific motion types. On the other hand, although the high-dimensional approach may recover the motions without learned models by sampling directly in the pose space, it lacks robustness and efficiency. Within the framework, the two parallel approaches, low- and high-dimensional, are fused via a probabilistic approach at each time step. This probabilistic fusion approach ensures that the overall performance of the system is improved by concentrating on the respective advantages of the two approaches and resolving their weak points. The experimental results, after qualitative and quantitative comparisons, demonstrate the effectiveness of the proposed approach in tracking generic human motion.

In our previous work, we reported a system that monitors an intersection using a network of horizontal laser scanners. This paper focuses on an algorithm for moving-object detection and tracking, given a sequence of distributed laser scan data of an intersection. The goal is to detect each moving object that enters the intersection; estimate state parameters such as size; and track its location, speed, and direction while it passes through the intersection. This work is unique, to the best of the authors' knowledge, in that the data is novel, which provides new possibilities but with great challenges; the algorithm is the first proposal that uses such data in detecting and tracking all moving objects that pass through a large crowded intersection with focus on achieving robustness to partial observations, some of which result from occlusions, and on performing correct data associations in crowded situations. Promising results are demonstrated using experimental data from real intersections, whereby, for 1063 objects moving through an intersection over 20 min, 988 are perfectly tracked from entrance to exit with an excellent tracking ratio of 92.9%. System advantages, limitations, and future work are discussed.

This work is motivated by the following two potential applications: 1) enhancing driving safety and 2) collecting traffic data in a large dynamic urban environment. A laser-scanner-based approach is proposed. The problem is formulated as a simultaneous localization and mapping (SLAM) with object tracking and classification, where the focus is on managing a mixture of data from both dynamic and static objects in a highly dynamic environment. A trajectory-oriented closure is also proposed using the sporadically available global positioning system (GPS) measurements in urban areas to assist for global accuracy, particularly when the vehicle makes a noncyclical measurement in a large outdoor environment. Experiments are conducted using the data that were collected along a course near 4.5 km in a highly dynamic environment. Possibilities of the approaches toward the two potential applications are demonstrated, and avenues for future works are discussed.

In this research, a novel system for monitoring an intersection using a network of single-row laser range scanners (subsequently abbreviated as "laser scanner") and video cameras is proposed. Laser scanners are set on the road side to profile an intersection horizontally from different viewpoints. The contour points of moving objects are captured at a certain horizontal plane with a high scanning rate (e.g., 37 Hz). A laser-based processing algorithm is developed, thus the moving objects entered the intersection are detected and tracked to estimate their state parameters, such as: location, speed, and direction at each time instance. In addition, laser data and processing results are forwarded to an associated video camera, so that a visualization as well as fusion-based processing can be achieved. An experiment in central Beijing is presented, demonstrating that a large quantity of physical dimension and detailed traffic data can be obtained through such a system.