Waqas Hassan

An adaptive sample count particle filter

The particle filter technique has been used extensively over the past few years to track objects in challenging environments. Due to its nonlinear nature and the fact that it does not assume a Gaussian probability density function it tends to outperform other available tracking methods. A novel adaptive sample count particle filter (ASCPF) tracking method is presented in this paper for which the main motivation is to accurately track an object in crowded scenes using fewer particles and hence with reduced computational overhead. Instead of taking a fixed number of particles, a particle range technique is used where an upper and lower bound for the range is initially identified. Particles are made to switch between an active and inactive state within this identified range. The idea is to keep the number of active particles to a minimum and only to increase this as and when required. Active contours are also utilized to determine a precise area of support around the tracked object from which the color histograms used by the particle filter can be accurately calculated. This, together with the variable particle spread, allows a more accurate proposal distribution to be generated while using less computational resource. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster.

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.

An improved background segmentation method for ghost removals

Video surveillance has become common for the maintenance of security in a wide variety of applications. However, the increasingly large amounts of data produced from multiple video camera feeds is making it increasingly difficult for human operators to monitor the imagery for activities likely to give rise to threats. This has led to the development of different automated surveillance systems that can detect, track and analyze video sequences both online and offline and report potential security risks. Segmentation of objects is an important part of such systems and numerous background segmentation techniques have been used in the literature. One common challenge faced by these techniques is adaption in different lighting environments. A new improved background segmentation technique has been presented in this where the main focus is to accurately segment potentially important objects by reducing the overall false detection rate. Historic edge maps and tracking results are analyzed for this purpose. The idea is to obtain an up to date edge map of the segmented region highlighted as foreground areas and compare them with the stored results. The edge maps are obtained using a novel adaptive edge orientation based technique where orientation of the edge is used. Experimental results have shown that the discussed technique gives over 85% matching results even in severe lighting changes.

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.

PHACT: Parallel HOG and Correlation Tracking

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce.

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.