Vehicle detection and tracking systems are nowadays under development for driver assistance systems. On-board sensors are used to alert the drivers about the driving environment and a possible collision with another vehicle. Vehicle detection techniques have gained importance for the last 15 years. A few months before, Google self-driving cars have done their first test drive. This has opened a new era of automatic self-driving in this field. The development of a reliable and successful system for vehicle detection and tracking is the main step. The main problem with the vehicle detection technique is the correct extraction of vehicles in consecutive video frames in complex outdoor environments such as illumination conditions, unpredictable interaction among traffic participants, and cluttered backgrounds. This becomes more challenging due to huge changes in vehicles appearance as they vary in size, color and shape. The appearance gets effected by nearby objects. Conventional background subtraction methods fail completely because of the changes in viewpoint from frame to frame. This process usually requires a near-real time response to what has been seen. In ideal conditions, we need each video or photo frame to be processed rapidly to give vehicle detection system enough response time for reaction under expected collision probabilities. This also requires the choice of sensors for detection. The sensors currently being used are lasers, radar, cameras and others. The data fusion among several sensors is also challenging. The aim of my research will be to develop a reliable vehicle detection and tracking system which will be robust in complex conditions.
The basic model for vehicle detection and tracking consists of the following three basic steps.
(i) Vehicle candidates generation
(ii) Generated candidates verification
(iii) Verified candidates tracking
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