In this paper, we aim to track objects that undergo abrupt appearance changes and heavy occlusions. To address these problems, we propose an online structure learning algorithm which contains two layers, block-based online random forest classifiers (BORFs) and online structure models (OSMs). BORFs are able to handle occlusion problems since they model local appearances of the target. To further improve the accuracy and reliability, the algorithm utilizes relational models as context information to combine BORFs into the online structure models. Capturing the discriminative parts of targets with online learnt structures, OSMs help locate targets accurately even when they are heavily occluded. In addition, OSMs guide the block occlusion reasoning and the update scheme of BORFs and relational models, which can handle appearance changes and drift problems effectively. Experiments on challenging videos show that the proposed tracker performs better than several state-of-the-art algorithms which demonstrate the effectiveness of our approach.

Vehicle detection in traffic scenes is a fundamental task for intelligent transportation system and has many practical applications as diverse as traffic monitoring, intelligent scheduling and autonomous navigation. In recent years, the number of detection approaches in monocular images has grown rapidly. However, most of them focus on detecting other objects (such as face, pedestrian, cat, dog, etc.) and also there lacks of vehicle datasets with various conditions for vehicle detection and comprehensive comparisons. To address these problems, we perform an extensive evaluation of many state-of-the-art detection approaches on vehicles. Our main contributions are: (1) we collect a large dataset of real-world vehicles in frontal/rear view with 30° ~ -30° yaw changes and 5° ~ 45° pitch changes under different weather conditions (snowy, rainy, sunny and cloudy) and illumination variations, and then (2) we evaluate six types of state-of-the-art features in Real AdaBoost framework on the adequate dataset collected by ourselves and a public dataset using the same evaluation protocol. Our study presents a fair comparison and deep analysis of these features in vehicle detection. From these experiments, we explore the characteristics of good features for vehicle detection. (3) Finally, we exploit these characteristics and propose a relatively effective and efficient detector, balancing performance, speed and memory cost which can be put into practical use.

Crowd counting which aims at obtaining the number of people within a scene is an important computer vision task. While most previous methods try to count people within one frame, this paper addresses this problem using the detection flow which is defined as a set of object detection responses along the temporal video sequence. We argue that counting based on detection flow provides a better way to estimate the crowd size with following merits: 1) it can greatly alleviate the common weakness of an object detector including miss detection and false alarms; 2) it is robust to temporal object occlusions and noises; 3) it is more competent to give specific descriptions of the crowd, e.g. crowd moving directions and target locations. Experiment results on PETS 2009 dataset demonstrate the potential of this method.

Background subtraction plays a key role in many surveillance systems. A good background subtractor should not only be able to robustly detect targets under different situations (e.g. moving and static), but also to adaptively maintain the background model against various influences (e.g. dynamic scenes and noises). This paper proposes a novel background modeling approach with these good characteristics. By introducing the “life span” concept into a background model, different properties of the scene are obtained through different life span models. Specifically, three different models, i.e., the Long Life Span Model, the Middle Life Span Model, and the Short Life Span Model, are online adaptively built and updated in a collaborative manner. Output of the system gives an adaptive, robust, and efficient estimation of the foreground region which can facility many practical applications. Experiment results on lots of surveillance videos demonstrate the superiority of the proposed method over competing approaches.

Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian inference approach which dynamically switches between an offline general model and an online dedicated model to deal with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey, demonstrate the effectiveness and efficiency of the proposed method.