Visual tracking is fragile in some difficult scenarios, for instance, appearance ambiguity and variation, occlusion can easily degrade most of visual trackers to some extent. In this paper, visual tracking is empowered with wireless positioning to achieve high accuracy while maintaining robustness. Fundamentally different from the previous works, this study does not involve any specific wireless positioning algorithms. Instead, we use the confidence region derived from the wireless positioning Cramér-Rao bound (CRB) as the search region of visual trackers. The proposed framework is low-cost and very simple to implement, yet readily leads to enhanced and robustified visual tracking performance in difficult scenarios as corroborated by our experimental results. Most importantly, it is utmost valuable for the practioners to pre-evaluate how effectively can the wireless resources available at hand alleviate the visual tracking pains.

Synchronized video editing system, or mashup generation from multiple synchronized videos, has gained much attention due to its high efficiency and low cost in processing videos to convey information. However, few of the existing methods focus on generating the personal mashup over a complete timeline from synchronized surveillance videos, which is increasingly demanded for effectively presenting personal activities without violating the privacy of others. To fill this gap, we develop a Reinforcement Learning (RL)-based personal mashup generation system, which assesses the frame quality at a semantic level and formulates the view selection as an RL problem to improve the efficiency in retrieving the mashups with arbitrary beginnings. Furthermore, we propose a framing objective to perform spatial editing, which enables the views to automatically zoom in and out, so as to present the target people more comprehensively. Both qualitative and quantitative analyses are presented to demonstrate the effectiveness of the proposed frame quality measurements, the RLbased algorithm, and the framing objective.