Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out

DETECTING AND COUNTING VEHICLES FROM SMALL LOW-COST UAV IMAGES

ABSTRACT In recent years, many civil users have been interested in unmanned aerial vehicle (UAV) for traffic monitoring and traffic data collection because they have the ability to cover a large area, focus resources on the current problems, travel at higher speeds than ground vehicles, and are not restricted to traveling on the road network. This paper presents a method for detecting and counting vehicles from UAV video flow. The algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes have been developed. In the algorithm, video frame-to-frame matching to track vehicle is one of important steps. Dynamic vehicles are identified using both background elimination and background registration techniques. The background elimination method uses concept of least squares to compare the accuracies of the current algorithm with the already existing algorithms. The background registration method uses background subtraction which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. In addition, because of high data sampling rates of video flow, resampling of video flow is also analyzed and discussed. The objective of this research is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow