Moving Vehicle Detection In Traffic Surveillance Using Mamoving Vehicle Detection In Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques
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Moving Vehicle Detection in Traffic Surveillance Using MaMoving Vehicle Detection in Traffic Surveillance Using Machine Learning Techniqueschine Learning Techniques
Intelligent Transport Systems (ITS) is the concept for the construction of a Transport Infrastructure that involves a driven data, telecommunications network for consumers, roads and automobiles. Video Detection is most widespread used in the Intelligent Transportation System (ITS) as it plays a key role in Transportation System. Dynamic modifications of background images due to environment, lightening, shadows make it inconvenient in detecting and tracking moving vehicles from the videos. Therefore, the moving background objects, brightness variation and vehicle adhesion create several challenges in the detection of moving vehicle. To minimize the problem, automatic moving vehicle detection is discussed in this thesis. This chapter consists of background of ITS system, Moving Vehicle System, Tracking Algorithms and applications. It also discusses the problems in Vehicle Detection System, followed by the Research Objective and Thesis Organization.
Road User Detection and Analysis in Traffic Surveillance Videos
Road user data collection and behaviour analysis has been an active research topic in the last decade. Automated solutions can be achieved based on video analysis with computer vision techniques. In this thesis, we propose a method to estimate traffic objects' locations with state-of-the-art vision features and learning models. Our focus is put on the applications of cyclist's helmet recognition and 3D vehicle localization. With limited human labelling, we adopt a semi-supervised learning process: tri-training with views of shapes and motion flow for vehicle detection. Experiments are conducted in real-world traffic surveillance videos.
Vehicle Detection and Tracking in Highway Surveillance Videos
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.