Surveillance Video Sequences Research Articles

Object Tracking in Structured Environments for Video Surveillance Applications

We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic monitoring, etc. In these applications, the movements of objects are constrained by structured environments. Therefore, the relationship between objects and environments can be exploited as additional information for improving the performance of tracking. We use the environment state to model the relationship between the objects and environments, and integrate it into the framework of Bayesian tracking. In this paper, distance transform is used to model the environment state, and particle filtering is employed as the paradigm for solving the Bayesian tracking problem. The adaptive dynamics model and environment prior are devised for the particle filter to fully utilize the environment information in the tracking process. Experiments on some video surveillance sequences demonstrate the effectiveness and robustness of our approach for tracking object motions in structured environments.

Kalman Filter Based Tracking in an Video Surveillance System

In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a con ...

Visual event recognition using decision trees

This paper presents a classifier-based approach to recognize dynamic events in video surveillance sequences. The goal of this work is to propose a flexible event recognition system that can be used without relying on a long-term explicit tracking procedure. It is composed of three stages. The first one aims at defining and building a set of relevant features describing the shape and movements of the foreground objects in the scene. To this aim, we introduce new motion descriptors based on space-time volumes. Second, an unsupervised learning-based method is used to cluster the objects, thereby defining a set of coarse to fine local patterns of features, representing primitive events in the video sequences. Finally, events are modeled as a spatio-temporal organization of patterns based on an ensemble of randomized trees. In particular, we want this classifier to discover the temporal and causal correlations between the most discriminative patterns. Our system is experimented and validated both on simulated and real-life data.

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.

Moving object detection in the H.264/AVC compressed domain for video surveillance applications

In this paper a novel method is presented to detect moving objects in H.264/AVC [T. Wiegand, G. Sullivan, G. Bjontegaard, G. Luthra, Overview of the H.264/AVC video coding standard, IEEE Transactions on Circuits and Systems for Video Technology, 13 (7) (2003) 560–576] compressed video surveillance sequences. Related work, within the H.264/AVC compressed domain, analyses the motion vector field to find moving objects. However, motion vectors are created from a coding perspective and additional complexity is needed to clean the noisy field. Hence, an alternative approach is presented here, based on the size (in bits) of the blocks and transform coefficients used within the video stream. The system is restricted to the syntax level and achieves high execution speeds, up to 20 times faster than the related work. To show the good detection results, a detailed comparison with related work is presented for different challenging video sequences. Finally, the influence of different encoder settings is investigated to show the robustness of our system.

Continuous variance estimation in video surveillance sequences with high illumination changes

Continuous estimation of signal statistics is an important issue in many video processing systems, such as motion detection in surveillance applications. In this paper we demonstrate how results of classical expressions for variance estimation decrease in accuracy when dealing with sequences containing high illumination variations. The paper also proposes a new estimation method, and shows how, under such conditions, the accuracy of the proposed method produces better results whilst maintaining performance in scenarios with smaller changes, thus improving the motion detection stage of a video surveillance system.

A new computer-assisted technique to aid personal identification

The paper describes a procedure aimed at identification from two-dimensional (2D) images (video-surveillance tapes, for example) by comparison with a three-dimensional (3D) facial model of a suspect. The application is intended to provide a tool which can help in analyzing compatibility or incompatibility between a criminal and a suspect's facial traits. The authors apply the concept of "geometrically compatible images". The idea is to use a scanner to reconstruct a 3D facial model of a suspect and to compare it to a frame extracted from the video-surveillance sequence which shows the face of the perpetrator. Repositioning and reorientation of the 3D model according to subject's face framed in the crime scene photo are manually accomplished, after automatic resizing. Repositioning and reorientation are performed in correspondence of anthropometric landmarks, distinctive for that person and detected both on the 2D face and on the 3D model. In this way, the superimposition between the original two-dimensional facial image and the three-dimensional one is obtained and a judgment is formulated by an expert on the basis of the fit between the anatomical facial districts of the two subjects. The procedure reduces the influence of face orientation and may be a useful tool in identification.

A locally tuned nonlinear technique for color image enhancement

An innovative technique for the enhancement of digital color images captured under extremely non-uniform lighting conditions is proposed in this paper. The key contributions of this technique are adaptive intensity enhancement, contrast enhancement and color restoration. Simultaneous enhancement of extreme dark and bright intensity regions in an image is performed by a specifically designed Locally Tuned Sine Non-Linear (LTSN) function. The intensity of each pixel's magnitude is tuned based on its surrounding pixels to accomplish contrast enhancement. Retrieval of the color information from the enhanced intensity image is achieved by a linear color restoration process which is based on the chromatic information of the input image. Experimental evaluations show that the proposed algorithm can be effectively used for improving the visibility of night time surveillance video sequences with frames having extreme bright and dark regions.

Structural similarity-based object tracking in multimodality surveillance videos

This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.

Image authentication techniques for surveillance applications

In automatic video surveillance (VS) systems, the issue of authenticating the video content is of primary importance. Given the ease with which digital images and videos can be manipulated, practically they do not have any value as legal proof, if the possibility of authenticating their content is not provided. In this paper, the problem of authenticating video surveillance image sequences is considered. After an introduction motivating the need for a watermarking-based authentication of VS sequences, a brief survey of the main watermarking-based authentication techniques is presented and the requirements that an authentication algorithm should satisfy for VS applications, are discussed. A novel algorithm which is suitable for VS visual data authentication is also presented and the results obtained by applying it to test data are discussed.