Video-based Surveillance Research Articles

Representation spaces in a visual-based human action recognition system

Visual tracking consists of locating or determining the configuration of a known object at each frame of a video sequence. Usually, the description of the whole scene involves the participation of multiple targets, their movements and interactions, etc., and the scenario particular features. This paper presents a visual tracking system framework oriented to provide a “near natural language” description of the involved targets in the scene actions. Our prototype focuses on the detection, tracking and feature extraction of a dynamic number of targets in a scenario along time. The design of any visual tracking system usually needs the injection of human knowledge at each transformed level of description, in order to produce from raw videos a linguistic scene summary. The main aim of this work was to make explicit the knowledge injection needed to link the low-level representations (associated to signals) to the high-level semantics (related to knowledge) in the visual tracking problem. As a result, the emerging semantic necessary at the two transformation level is analysed and presented. We have concentrated on the representation spaces for the memetic algorithm particle filter applied to multiple object tracking in annotated scenarios, oriented to video-based surveillance applications. Finally, some example applications on different surveillance scenarios are presented and discussed.

Improving target detection by coupling it with tracking

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where target detection is integrated with tracking to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other, we feed the results of tracking back to the detection stage in order to adaptively optimize the detection threshold and improve system robustness. First, the initial target locations are extracted using background subtraction. To model the background, we employ Support Vector Regression (SVR) which is updated over time using an on-line learning scheme. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and improve the confidence level of detection. For verification, additional information based on size, color and motion information is utilized. Feeding back the results of tracking to the detection stage restricts the range of detection threshold values, suppresses false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing perspective projection distortions. We have validated the proposed framework in two different application scenarios, one detecting vehicles at a traffic intersection using visible video and the other detecting pedestrians at a university campus walkway using thermal video. Our experimental results and comparisons with frame-based detection and kernel-based tracking methods illustrate the robustness of our approach.

Framework for Real-Time Behavior Interpretation From Traffic Video

Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions.

Active video-based surveillance system: the low-level image and video processing techniques needed for implementation

The importance of video surveillance techniques has considerably increased since the latest terrorist incidents. Safety and security have become critical in many public areas, and there is a specific need to enable human operators to remotely monitor the activity across large environments. For these reasons, multicamera systems are needed to provide surveillance coverage across a wide area, ensuring object visibility over a large range of depths. In the development of advanced visual-based surveillance systems, a number of key issues critical to its successful operation must be addressed. This article describes the low-level image and video processing techniques needed to implement a modern surveillance system. In particular, the change detection methods for both fixed and mobile cameras (pan and tilt) are introduced and the registration methods for multicamera systems with overlapping and nonoverlapping views are discussed.

A real-time algorithm for error recovery in remote video-based surveillance applications

This work presents a real-time post-processing error-recovery algorithm explicitly devoted at enhancing the performances of outdoor video-surveillance systems working in remote modality. The aim of the proposed algorithm is to distinguish between changed blocks due to variations in the observed scene and noise-altered blocks that contain errors caused by channel noise. Such errors can be corrected by directly exploiting the considerable spatio-temporal redundancy of the encoded digital source without using any additional information. Experimental results, obtained through colour JPEG transmission simulations performed in the context of an actual remote video-surveillance system, compare the proposed scheme with different concealment schemes. It is proven that substantial improvements both in terms of perceptual quality and performance of the overall video-surveillance system are possible, by using the proposed algorithm as a stand-alone module or in conjunction with a relatively low-redundancy FEC coding.

Performance Evaluation Criterion for Characterizing Video-Surveillance Systems

In the last decade, video surveillance systems have been developed to guard remote environments in order to detect and prevent dangerous situations. In general, such systems are very complex and their performances rely strongly on the values taken on by the parameters regulating the behavior of surveillance algorithms. This makes it difficult to compare performances to select the most suitable system for the problem considered. Moreover, parameters are set during the installation phase, but a good selection requires time and experience. In this paper, we present an evaluation method for characterizing video-surveillance systems in order to provide an easy way of system installation. The proposed procedure is based on the definition of Receiver Operating Characteristic (ROC) curves traced for different parameter sets and for different system working conditions. Examples are given that concern an indoor video-based surveillance system for detecting, recognizing and tracking moving objects; the system has been characterized and evaluated for different scenarios. Results prove the validation of the proposed evaluation procedure though a comparison of the performances obtained in a laboratory with the ones achieved in real working environments, after system installation following the guidelines provided by the proposed methodology.