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The research analyzes the operating performance of three vehicle detection technologies for use in the City of Winnipeg. The technologies were: Autoscope Encore (video sensor), Iteris Vantage Edge2 (video sensor) and Matrix Wavetronix (microwave sensor). The sensors were tested in the tow eastbound lanes and two turning lanes on the intersection of Bishop Grandin Blvd and St.Mary's Road in Winnipeg, Manitoba. The research considered 24 weather, illumination, wind and traffic conditions. Testing and analysis was completed at the stop bar, and advance zone as well as for count performance. Sensitivity is a measure of the number of calls missed by the sensor. In terms of sensitivity, Iteris performed best overall, performing with greater sensitivity than Autoscope and Matrix in 17 of 24 conditions at the stop bar and outperforming in 11 of 12 conditions for advanced zone detection in this research. For count performance the Iteris had better accuracy when compared to ground truth established by Miovision Technologies, than Autoscope and Matrix.

The research analyzes the operating performance of three vehicle detection technologies for use in the City of Winnipeg. The technologies were: Autoscope Encore (video sensor), Iteris Vantage Edge2 (video sensor) and Matrix Wavetronix (microwave sensor). The sensors were tested in the tow eastbound lanes and two turning lanes on the intersection of Bishop Grandin Blvd and St.Mary's Road in Winnipeg, Manitoba. The research considered 24 weather, illumination, wind and traffic conditions. Testing and analysis was completed at the stop bar, and advance zone as well as for count performance. Sensitivity is a measure of the number of calls missed by the sensor. In terms of sensitivity, Iteris performed best overall, performing with greater sensitivity than Autoscope and Matrix in 17 of 24 conditions at the stop bar and outperforming in 11 of 12 conditions for advanced zone detection in this research. For count performance the Iteris had better accuracy when compared to ground truth established by Miovision Technologies, than Autoscope and Matrix.

This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario.

We present a novel approach for vehicle detection and tracking in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically "learned" from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a video-specific classifier is trained with these examples. Both the background model and the trained classifier are used in conjunction to detect vehicles in a frame. Tracking is achieved by a simplified multi-hypotheses approach. An over-complete set of tracks is created considering every observation within a time interval. As needed hypothesized detections are generated to force continuous tracks. Finally, a scoring function is used to separate the valid tracks in the over-complete set. The proposed detection and tracking algorithm is tested in a challenging application; vehicle counting. Our method achieved very accurate results in three traffic surveillance videos that are significantly different in terms of view-point, quality and clutter. / text

Based on .Net Framework4.0 development platform and Visual C# language, this thesis presents various methods of performing lane detection and preceding vehicle detection/tracking with code optimization and acceleration to reduce the execution time. The thesis consists of two major parts: vehicle detection and tracking. In the part of detection, driving lanes are identified first and then the preceding vehicles between the left lane and right lane are detected using the shadow information beneath vehicles. In vehicle tracking, three-pass search method is used to find the matched vehicles based on the detection results in the previous frames. According to our experiments, the preprocessing (including color-intensity conversion) takes a significant portion of total execution time. We propose different methods to optimize the code and speed up the software execution using pure C # pointers, OPENCV, and OPENCL etc. Experimental results show that the fastest detection/tracking speed can reach more than 30 frames per second (fps) using PC with i7-2600 3.4Ghz CPU. Except for OPENCV with execution rate of 18 fps, the rest of methods have up to 28 fps processing rate of almost the real-time speed. We also add the auxiliary vehicle information, such as preceding vehicle distance and vehicle offset warning.

The thesis develops a driving assistant system that can locate the positions of the lane boundaries and detects the existence of the front-vehicle. It can also provide warning mechanism so as to avoid the danger as possible collides with previous vehicle. In lane recognition, we utilized the largest gradient of luminance value to detect possible road surface marking, then cooperated with the marking static and dynamic behavior of road surface characteristic to set up road surface marking and detect system. On the other hand, we considered vehicle detection leach the vehicle bottom shade characteristic from dynamic area threshold processing, and then judge and label where the vehicle exits. By the principle of the optics image formation, we estimated the relative distance from the previous vehicle. In this thesis, we proposed an easy and fast measure for previous vehicle of 96% correct rate in different environment. Running on typical 1.7Ghz processor system results up to 80 frames per second.

The Traffic Situation Estimator is a method that analyses vehicle behaviour by monitoring and counting the surrounding traffic. This is done with image analysis that keepstrack of several vehicles through consecutive frames under good lightning conditionson a straight one way road. The behaviour of the detected vehicles is then analysedin a state machine driven counter to estimate the traffic rhythm and determine if thedetected vehicles are approaching, getting away, have been overtaken or have overtakenthe ego-vehicle. Depending on the result the Traffic Situation Estimator suggest different reactions helping the driver to follow the traffic rhythm which will improve safetyand the energy efficiency. If the user is not following the traffic rhythm the applicationwill give advice to the user how to adapt to the traffic rhythm by driving faster, sloweror optionally suggest to overtake vehicles ahead.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents a method to accurately determine the location and amount of pedestrians detected by different vehicles equipped with a Pedestrian Autonomous Emergency Braking (PAEB) system, taking into consideration the inherent inaccuracy of the pedestrian sensing from these vehicles. In the thesis, a mathematical model of the pedestrian information generated by the PAEB system in the V2V network is developed. The Greedy-Medoids clustering algorithm and constrained hierarchical clustering are applied to recognize and reconstruct actual pedestrians, which enables a subject vehicle to approximate the number of the pedestrians and their estimated locations from a larger number of pedestrian alert messages received from many nearby vehicles through the V2V network and the subject vehicle itself. The proposed methods determines the possible number of actual pedestrians by grouping the nearby pedestrians information broadcasted by different vehicles and considers them as one pedestrian. Computer simulations illustrate the effectiveness and applicability of the proposed methods. The results are more integrated and accurate information for vehicle Autonomous Emergency Braking (AEB) systems to make better decisions earlier to avoid crashing into pedestrians.