Globally Optimal Object Tracking with Fully Convolutional Networks

Abstract

Tracking is one of the most important but still difficult tasks in computer vision and pattern recognition. The main difficulties in the tracking field are appearance variation and occlusion. Most traditional tracking methods set the parameters or templates to track target objects in advance and should be modified accordingly. Thus, we propose a new and robust tracking method using a Fully Convolutional Network (FCN) to obtain an object probability map and Dynamic Programming (DP) to seek the globally optimal path through all frames of video. Our proposed method solves the object appearance variation problem with the use of a FCN and deals with occlusion by DP. We show that our method is effective in tracking various single objects through video frames.

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

“…Visual object tracking algorithms can be categorized as single-object vs. multiple-object trackers, generative vs. discriminative, context-aware vs. nonaware, and online vs. offline learning algorithms. Single object trackers [93], [95], [148] are the algorithms tracking only one object in the sequence, while multi-object trackers [13], [92], [127], [167] simultaneously track multiple targets and follow their trajectories. In generative models, the tracking task is carried out via searching the best-matched window, while discriminative models discriminate target patch from the background [131], [168], [177].…”