Vision-Based Production of Personalized Video

In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach

Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

Multisensor data fusion for joint people tracking and identification with a service robot

Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.Comment: has been published on IEEE TI