A model-based approach for combined tracking and resolution enhancement of faces in low resolution video

Wide area surveillance situations require many sensors, thus making the use of highresolution cameras prohibitive because of high costs and exponential growth in storage. Small and low cost CCTV cameras may produce poor quality video, and high-resolution CCD cameras in wide area surveillance can still yield low-resolution images of the object o

Automatic Fitting of a Deformable Face Mask Using a Single Image

We propose an automatic method for person-independent fitting of a deformable 3D face mask model under varying illumination conditions. Principle Component Analysis is utilised to build a face model which is then used within a particle filter based approach to fit the mask to the image. By subdividing a coarse mask and using a novel texture mapping technique, we further apply the 3D face model to fit into lower resolution images. The illumination invariance is achieved by representing each face as a combination of harmonic images within the weighting function of the particle filter. We demonstrate the performance of our approach on the IMM Face Database and the Extended Yale Face Database and show that it out performs the Active Shape Models approach

Learning and Cooperating Multi-Agent Scheduling Repair Using a Provenance-Centred Approach

The timetabling problem is to find a timetable solution by assigning time and resources to sessions that satisfy a set of constraints. Traditionally, research has focused on optimization towards a final solution but this paper focuses on minimizing disturbance impact due to changing conditions. A Multi-Agent System (MAS) is proposed in which users are represented as autonomous agents negotiating with one another to repair a timetable. From repeated negotiations, agents learn to develop a model of other agent's preferences. The MAS is simulated on a factorial experiment set up and varying the cooperation level, learning model and selection strategy. A provenance-centred approach is adopted to improve the human aspect of timetabling to allow users to derive the steps towards a solution and make changes to influence the solution

PDA Based Human Motion Recognition System

10.1142/S021819400500218XInternational Journal of Software Engineering and Knowledge Engineering152199-204ISEK

Pedestrian detection for mobile bus surveillance

In this paper, we present a system for pedestrian detection involving scenes captured by mobile bus surveillance cameras in busy city streets. Our approach integrates scene localization, foreground and background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data. In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarities and second stage further clusters these aligned frames in terms of lighting. This produces clusters of images which are differential in viewpoint and lighting. A kernel density estimation (KDE) method for colour and gradient foreground-background separation are then used to construct background model for each image cluster which is subsequently used to detect all foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be identified. We have tested our system on a set of real bus video datasets and the experimental results verify that our system works well in practice.<br /

Multiple Views Tracking of Maritime Targets

This paper explores techniques for multiple views target tracking in a maritime environment using a mobile surveillance platform. We utilise an omnidirectional camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to estimate the platform?s ego-motion. For each target a part of the omnidirectional video is extracted, forming a corresponding set of virtual cameras. Each target is then tracked using a dynamic template matching method and particle filtering. Its predictions are then used to continuously adjust the orientations of the virtual cameras, keeping a lock on the targets. We demonstrate the performance of the application in several real-world maritime settings

Passenger monitoring in moving bus video

In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems.<br /

Comparative evaluation of pedestrian detection methods for mobile bus surveillance

We present a comparative evaluation of the state-of-art algorithms for detecting pedestrians in low frame rate and low resolution footage acquired by mobile sensors. Four approaches are compared: a) The Histogram of Oriented Gradient (HoG) approach [1]; b) A new histogram feature that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters [2] corresponding to the quantised orientation, called Histogram of Oriented Gradient Banks (HoGB) approach; c) The codebook based HoG feature with branch-and-bound (efficient subwindow search) algorithm [3] and; d) The codebook based HoGB approach. Results show that the HoG based detector achieves the highest performance in terms of the true positive detection, the HoGB approach has the lowest false positives whilst maintaining a comparable true positive rate to the HoG, and the codebook approaches allow computationally efficient detection.<br /