"One ability of the human visual system is the ability to identify and track moving objects. Examples of this can easily be seen in any sporting event. Humans are able to find an object in motion and track its current path and even predict a trajectory based on its current motion. Computer vision systems exist that are able to track an object in video, but usually these systems need to be instructed what the object to track is. As a way to further the work done by these computer vision systems, I present two additions to the work in the form of Adaptive Thresholding, a way to dynamically discover a threshold value of difference images, and a new method of blob tracking to further improve the accuracy of tracking blobs in video."--Abstract.

Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the shop; and what are the frequent paths), and to measure operational efficiency for improving customer experience. Video analytics has the enormous potential for non-security oriented commercial applications. This book presents the latest developments on video analytics for business intelligence applications. It provides both academic and commercial practitioners an understanding of the state-of-the-art and a resource for potential applications and successful practice.

Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS), Optical Flow (OF), etc. An important problem for performance evaluation is the absence of any stable and flexible software for comparison of different algorithms. In this frame, we have designed and implemented the software for comparing and evaluating the well-known video object D&T algorithms on the same platform. This software is able to compare them with the same metrics in real-time and on the same platform. It also works as an automatic and/or semi-automatic test environment in real-time, which uses the image and video processing essentials, e.g. morphological operations and filters, and ground-truth (GT) XML data files, charting/plotting capabilities, etc. Along with the comprehensive literature survey of the abovementioned video object D&T algorithms, this book also covers the technical details of our performance benchmark software as well as a case study on people D&T for the functionality of the software.

Object tracking could be a terribly difficult task within the presence of variability illumination condition, background motion, complicated object form, partial and full object occlusions. The main intention of an object trailer is to make the path of an object over time by characteristic its position in all frames of the video. This book is intended to educate the researchers in the field of tracking of moving object(s) in a video sequence. This book provides a path for the researchers to identify the works done by others in the same field and thereby to figure out the gap in the current knowledge. This book is organized into three Modules. Module 1 talks about the introduction of object detection and tracking. Module 2 discusses about the various studies of object tracking and motion detection. The views of the various authors about this hot research topic are discussed in this Module and Module 3 gives the conclusion of the entire research review.Object tracking could be a terribly difficult task within the presence of variability illumination condition, background motion, complicated object form, partial and full object occlusions. The main intention of an object trailer is to make the path of an object over time by characteristic its position in all frames of the video. This book is intended to educate the researchers in the field of tracking of moving object(s) in a video sequence. This book provides a path for the researchers to identify the works done by others in the same field and thereby to figure out the gap in the current knowledge. This book is organized into three Modules. Module 1 talks about the introduction of object detection and tracking. Module 2 discusses about the various studies of object tracking and motion detection. The views of the various authors about this hot research topic are discussed in this Module and Module 3 gives the conclusion of the entire research review.

"There is a growing interest in the computer vision community towards video understanding, in particular towards visual event recognition ... This dissertation surveys different taxonomies of motion understanding problems, identifies the major components in an automated visual event recognition system, and presents the challenges and the significant studies in moving object detection, shadow elimination, and object tracking. Novel schemes for shadow detection and object tracking are proposed and implemented. The proposed shadow detection scheme does not rely on models of scene or objects, which makes it robust for a variety of outdoor surveillance applications, and also successfully eliminates problems due to illumination changes that are common in outdoor sequences. The proposed schemes for object tracking address the problem of correspondence in the presence of multiple moving objects and occlusions in the scene, and involve multi-hypothesis decision making and color appearance models"--Abstract, leaf iii.

This dissertation, "Novel Techniques for Visual Object Tracking and Depth-aware Video Processing" by Shuai, Zhang, 張帥, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Visual object tracking is frequently employed in applications, such as intelligent video surveillance, human body tracking, and many other related problems. Therefore, it is a fundamental problem in video processing and computer vision. The general procedure of automatic object tracking consists of object detection, object representation, tracking strategy and model updating. Tracking strategy, in particular, is an important component because it performs prediction and inference of useful object information such as object location, object orientation and object size, from one frame to another. In this dissertation, a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM) tracking strategy is proposed. The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional Kalman filters using GM. As the GM is simplified directly without resampling using particles, the proposed BKF-SGM considerably reduces the exponential arithmetic complexity and avoids performance degradation due to sampling degeneracy and impoverishment in conventional particle filtering (PF). When coupled with an improved mean shift (MS) algorithm, the original MS tracker is extended under the BKF-SGM framework above to a bank of parallel MS trackers, which offer a more robust tracking performance. The resultant algorithm, which is called the BKF-SGM with improved MS (BKF-SGM-IMS), is inherently parallel in nature and hence can be readily accelerated using Graphics Processing Unit (GPU) to meet the high computational requirement in real-time applications. The proposed BKF-SGM-IMS algorithm can successfully handle complex scenarios with good performance and low arithmetic complexity. Moreover, the performance of both non-training/training-based object recognition algorithms can be improved by using our tracking results as input. As depth information make machine vision one step closer to human vision by combining color and depth information, there is a recent interest in depth-aware video processing and computer vision both in the academic and industrial fields. However, high quality and high resolution depth map acquisition for real world scene is a challenging problem. Conventional depth acquisition algorithms which rely on stereo/multi-view vision (passive method) or depth sensing device (active method) alone are limited by complicated scenes or imperfections of the depth sensing devices. In this dissertation, a new system for indoor high resolution and high quality depth estimation using joint fusion of stereo and depth sensing data is proposed. By modeling the observations using Markov random field (MRF), the fusion problem is formulated as a maximum a posteriori probability (MAP) estimation problem. The reliability and the probability density functions for describing the observations from the two devices are also derived. The MAP problem is solved using a multi-scale belief propagation (BP) algorithm. To suppress possible estimation noise, the depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results and numerical comparisons show that our system can provide high quality and high resolution depth maps, thanks to the complementary str