Real Time Tracking and Identification Of Moving Persons By Using A Camera In Outdoor Environment

A new method for detecting and tracking of moving persons based on low resolution image employing peripheral increment sign correlation image and identifying the moving persons by their color and spatial information is proposed in this paper. Many tracking algorithms have better performance under a static background in indoor environ-ment. It is, however, most of the tracking algorithms are applied in outdoor environment with noisy background instead of indoor environment. Since a low resolution image has a property that it can remove the small size pixels, it is adopted to solve the problem of the noisy background. In the tracking of a target object, many applications have problem when object occlude each other. A block matching technique based on peripheral incre-ment sign correlation image is utilized to solve this problem. The identi_cation of a target object is performed using color and spatial information of the target object. The experimental results prove the feasibility and usefulness of the proposed metho

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Morphological change detection algorithms for surveillance applications

Vision-based systems for remote surveillance usually involve change detection algorithms for intruders, obstacles or irregularities detection. In particular, there is a potentially very cost-effective approach to perform inspection with autonomous robot navigation, computer vision, and change detection based on automatic image registration and subtraction. In these cases, a model of the working environment is compared with data acquired during the system functioning in order to extract region of interests. Real time applications require simple, fast and reliable algorithms and methodologies presented in the literature show that morphological change detection satisfies these requirements. In this paper novel morphological algorithms for scene change detection are introduced; the proposed methods allow to obtain a system whose performances are relatively stationary also with varying environmental condition.