Adaptive object segmentation and tracking

Efficient tracking of deformable objects moving with variable velocities is an important current research problem. In this thesis a robust tracking model is proposed for the automatic detection, recognition and tracking of target objects which are subject to variable orientations and velocities and are viewed under variable ambient lighting conditions. The tracking model can be applied to efficiently track fast moving vehicles and other objects in various complex scenarios. The tracking model is evaluated on both colour visible band and infra-red band video sequences acquired from the air by the Sussex police helicopter and other collaborators. The observations made validate the improved performance of the model over existing methods. The thesis is divided in three major sections. The first section details the development of an enhanced active contour for object segmentation. The second section describes an implementation of a global active contour orientation model. The third section describes the tracking model and assesses it performance on the aerial video sequences. In the first part of the thesis an enhanced active contour snake model using the difference of Gaussian (DoG) filter is reported and discussed in detail. An acquisition method based on the enhanced active contour method developed that can assist the proposed tracking system is tested. The active contour model is further enhanced by the use of a disambiguation framework designed to assist multiple object segmentation which is used to demonstrate that the enhanced active contour model can be used for robust multiple object segmentation and tracking. The active contour model developed not only facilitates the efficient update of the tracking filter but also decreases the latency involved in tracking targets in real-time. As far as computational effort is concerned, the active contour model presented improves the computational cost by 85% compared to existing active contour models. The second part of the thesis introduces the global active contour orientation (GACO) technique for statistical measurement of contoured object orientation. It is an overall object orientation measurement method which uses the proposed active contour model along with statistical measurement techniques. The use of the GACO technique, incorporating the active contour model, to measure object orientation angle is discussed in detail. A real-time door surveillance application based on the GACO technique is developed and evaluated on the i-LIDS door surveillance dataset provided by the UK Home Office. The performance results demonstrate the use of GACO to evaluate the door surveillance dataset gives a success rate of 92%. Finally, a combined approach involving the proposed active contour model and an optimal trade-off maximum average correlation height (OT-MACH) filter for tracking is presented. The implementation of methods for controlling the area of support of the OT-MACH filter is discussed in detail. The proposed active contour method as the area of support for the OT-MACH filter is shown to significantly improve the performance of the OT-MACH filter's ability to track vehicles moving within highly cluttered visible and infra-red band video sequence

Tracking of motor vehicles from aerial video imagery using the OT-MACH correlation filter

Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions

Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods

Tracking Moving Objects Using Bandpass Filter Enhanced Localisation and Automated Initialisation of Active Contour Snakes

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Object tracking in a multi camera environment

Tracking objects in multi camera environments is an important requirement for video surveillance applications. A new active particle filter based tracking technique is presented, where objects are tracked across different cameras using a reduced number of particles. In order to cope with sudden colour and scale changes, a variable standard deviation value for spreading the particles is proposed. As the object moves from one scene to another, the number of particles along with the spread value is increased to minimize any effect of scale and colour change. The technique has been tested on live feeds from two different cameras and with scenes from the PETS dataset. The results have been compared with standard particle filtering techniques. It was found that not only did the proposed method result in almost similar tracking results but there is a 70% reduction in computational cost. © 2011 IEEE

Door surveillance using edge-map based Harris corner detector and active contour orientation

Accurately generating an alarm for a moving door is a precondition for tracking, recognizing and segmenting objects or people entering or exiting the door. The challenge of generating an alarm when a door event occurs is difficult when dealing with complex doors, moving cameras, objects moving or an obscured entrance of the door, together with the presence of varying illumination conditions such as a door-way light being switched on. In this paper, we propose an effective method of tracking the door motion using edge-map information contained within a localised region at the top of the door. The region is located where the top edge of the door displaces every time the door is opened or closed. The proposed algorithm uses the edge-map information to detect the moving corner in the small windowed area with the help of a Harris corner detector. The moving corner detected in the selected region gives an exact coordinate of the door corner in motion, thus helping in generating an alarm to signify that the door is being opened or closed. Additionally, due to the prior selection of the small region, the proposed method nullifies the adverse effects mentioned above and helps prevent different objects that move in front of the door affecting its efficient tracking. The proposed overall method also generates an alarm to signify whether the door was displaced to provide entry or exit. To do this, an active contour orientation is computed to estimate the direction of motion of objects in the door area when an event occurs. This information is used to distinguish between objects and entities entering or exiting the door. A Hough transform is applied on a specific region in the frame to detect a line, which is used to perform error correction to the selected windows. The detected line coordinates are used to nullify the effects of a moving camera platform, thus improving the robustness of the results. The developed algorithm has been tested on all the Door Zone video sequences contained with the United Kingdom Home Office i-LIDs dataset, with promising results

Tracking illegally parked vehicles using correlation of multi-scale difference of Gaussian filtered patches

Detection and tracking of illegally parked vehicles are usually considered as crucial steps in the development of a video-surveillance based traffic-management system. The major challenge in this task lies in making the tracking phase illumination-change tolerant. The paper presents a two-stage process to detect vehicles parked illegally and monitor these in subsequent frames. Chromaticity and brightness distortion estimates are used in the first stage to segment the foreground objects from the remainder of the scene. The process then locks onto all stationary 'vehicle'-size patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the illegally parked vehicles detected during the first stage. All the locked patches are filtered using a difference-of-Gaussian (DoG) filter operated at three different scales to capture a broad range of information. In succeeding frames patches at the same locations are similarly DoG filtered at the three different scales and the results matched with the corresponding ones initially generated. A combined score based on correlation estimates is used to track and confirm the existence of the illegally parked vehicles. Use of the DoG filter helps in extracting edge based features of the patches thus making the tracking process broadly illumination-invariant. The two-stage approach has been tested on the United Kingdom Home Office iLIDS dataset with encouraging results

Human detection using OT-MACH filter in cluttered FLIR imagery

An improvement to the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter with the addition of a Rayleigh distribution filter has been used to detect humans in FLIR imagery scenes. The Rayleigh distribution filter is applied to the OT-MACH filter to provide a sharper low frequency cut-off which improves the OT-MACH filter performance in terms of target discrimination. The OT-MACH filter has been trained using a Computer Aided Design (CAD) model and tested on the corresponding real target object in high clutter environments acquired from a Forward Looking Infra Red (FLIR) sensor. Evaluation of the performance of the Rayleigh modified OT-MACH filter is reported for the recognition of humans present within the thermal infra-red image data set

Approximate bandpass and frequency response models of the difference of Gaussian filter

The Difference of Gaussian (DOG) filter is widely used in optics and image processing as, among other things, an edge detection and correlation filter. It has important biological applications and appears to be part of the mammalian vision system. In this paper we analyse the filter and provide details of the full width half maximum, bandwidth and frequency response in order to aid the full characterisation of its performance. © 2010 Elsevier B.V. All rights reserved