Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand

Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications

GLOBAL CHANGE REACTIVE BACKGROUND SUBTRACTION

Background subtraction is the technique of segmenting moving foreground objects from stationary or dynamic background scenes. Background subtraction is a critical step in many computer vision applications including video surveillance, tracking, gesture recognition etc. This thesis addresses the challenges associated with the background subtraction systems due to the sudden illumination changes happening in an indoor environment. Most of the existing techniques adapt to gradual illumination changes, but fail to cope with the sudden illumination changes. Here, we introduce a Global change reactive background subtraction to model these changes as a regression function of spatial image coordinates. The regression model is learned from highly probable background regions and the background model is compensated for the illumination changes by the model parameters estimated. Experiments were performed in the indoor environment to show the effectiveness of our approach in modeling the sudden illumination changes by a higher order regression polynomial. The results of non-linear SVM regression were also presented to show the robustness of our regression model

Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance

Using the discrete hadamard transform to detect moving objects in surveillance video

In this paper we present an approach to object detection in surveillance video based on detecting moving edges using the Hadamard transform. The proposed method is characterized by robustness to illumination changes and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications. In addition to presenting an approach to moving edge detection using the Hadamard transform, we introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value (H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges. Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing and outperforms existing edge-based approaches in terms of both detection results and computational complexity

Visible spectrum extended-focus optical coherence microscopy for label-free sub-cellular tomography

We present a novel extended-focus optical coherence microscope (OCM) attaining 0.7 {\mu}m axial and 0.4 {\mu}m lateral resolution maintained over a depth of 40 {\mu}m, while preserving the advantages of Fourier domain OCM. Our method uses an ultra-broad spectrum from a super- continuum laser source. As the spectrum spans from near-infrared to visible wavelengths (240 nm in bandwidth), we call the method visOCM. The combination of such a broad spectrum with a high-NA objective creates an almost isotropic 3D submicron resolution. We analyze the imaging performance of visOCM on microbead samples and demonstrate its image quality on cell cultures and ex-vivo mouse brain tissue.Comment: 15 pages, 7 figure

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Micro Fourier Transform Profilometry (μ\muFTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $\mu$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.Comment: This manuscript was originally submitted on 30th January 1

Moving object detection using registration for a moving camera platform

In this research work, an accurate and fast moving object detector that can detect all the moving objects from Unmanned Aerial Platform (UAV) is proposed. Because of the distance of the UAV to the objects and the movement of the platform, object detection is a challenging task. In order to achieve best results with low error, at first the camera motion has to be estimated so, by using the Rosten and Drummond technique the corners is detected and then by using the corners the camera motion is compensated. After motion compensation, by subtracting the registered frame from the reference frame all the moving objects are detected and extracted