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

Automatic object classification for surveillance videos.

PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video and Wide Aerial Motion Imagery.Comment: PhD Dissertation (162 pages

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Contributions for the automatic description of multimodal scenes

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition