The active contour models with level set evolution have been visited with a vast number of methods for image segmentation. They can be mainly classified into region-based and edge-based models, and it has been validated that the hybrid variants combining both region and edge information can improve the segmentation performance. However, to the best of our knowledge, the theoretical foundation of collaboration mechanism between the region and the edge information is limited. Specifically, most existing hybrid models are just combining all the energy terms together, resulting in great challenges of choosing an appropriate weight coefficient for each term and accommodating different modalities of imaging. To overcome these difficulties, this paper proposes a region and edge synergetic level set framework named RESLS. It provides an approach to construct new hybrid level set models using a normalized intensity indicator function that allows the region information easily embedding into the edge-based model. In this case, the energy weights of region and edge terms can be constrained by the global optimization condition deduced from the framework. Some representative as well as state-of-the-art models are taken as examples to demonstrate the generality of our method. The experiments validate that under the guidance of the optimization condition, the weighting parameter of each term can be reliably chosen. Meanwhile, the segmentation accuracy, robustness, and computational efficiency of RESLS can be improved compared with its component models.

Online multi-object tracking remains a difficult problem in complex scenes because of inaccurate detections, frequent occlusions by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a hybrid data association strategy combined with instance segmentation and online feature learning to handle these difficulties effectively. First, we utilize an instance segmentation algorithm to separate each object from other objects and backgrounds in a pixel-to-pixel manner, which will help resolve typical difficulties in multi-object tracking, such as ID-switches and track drifting. Moreover, we propose a local-to-global hybrid data association strategy to take advantage of the superiorities of both online and batch data association methods. The local data association between observations in consecutive frames reduces the computational complexity, hence ensuring the efficiency of online tracking. The global data association complements the local data association by integrating multiple video frames, thus alleviating the fragmented tracklets. Last, to improve the appearance discriminability and make it more robust in dealing with appearance variations during tracking, a lightweight semantic-preserving hashing algorithm has been proposed to learn compact hash codes online. Experiments with the MOT17 Challenge dataset demonstrate the superior performance of the proposed approach over other state-of-the-art batch and online tracking methods.