A Cognitive Control-Inspired Approach to Object Tracking

Collaboration calibration and three-dimensional localization in multi-view system

In this research, the authors have addressed the collaboration calibration and real-time three-dimensional (3D) localization problem in the multi-view system. The 3D localization method is proposed to fuse the two-dimensional image coordinates from multi-views and provide the 3D space location in real time. It is a fundamental solution to obtain the 3D location of the moving object in the research field of computer vision. Improved common perpendicular centroid algorithm is presented to reduce the side effect of the shadow detection and improve localization accuracy. The collaboration calibration is used to generate the intrinsic and extrinsic parameters of multi-view cameras synchronously. The experimental results show that the algorithm can complete accurate positioning in indoor multi-view monitoring and reduce the complexity.

Greedy Batch-Based Minimum-Cost Flows for Tracking Multiple Objects

Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object tracking. However, they rely on the whole image sequence as input. When deployed in real-time applications or in distributed settings, these algorithms first operate on short batches of frames and then stitch the results into full trajectories. This decoupled strategy is prone to errors because the batch-based tracking errors may propagate to the final trajectories and cannot be corrected by other batches. In this paper, we propose a greedy batch-based minimum-cost flow approach for tracking multiple objects. Unlike existing approaches that conduct batch-based tracking and stitching sequentially, we optimize consecutive batches jointly so that the tracking results on one batch may benefit the results on the other. Specifically, we apply a generalized minimum-cost flows (MCF) algorithm on each batch and generate a set of conflicting trajectories. These trajectories comprise the ones with high probabilities, but also those with low probabilities potentially missed by detectors and trackers. We then apply the generalized MCF again to obtain the optimal matching between trajectories from consecutive batches. Our proposed approach is simple, effective, and does not require training. We demonstrate the power of our approach on data sets of different scenarios.