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the objective of this work is to compute the Time-to-Collision (TTC) of surrounding vehicles of a vehicle using motion information in driving video. The key advantage in this work is the extraction of potential danger without vehicle detection and recognition in prior, but directly from the motion divergence in the video. We analyze the trace expansion both horizontally and vertically condensed in...
Numerous accidents can be avoided if drivers are alerted just a few seconds before a collision. However, collision prediction is challenging because of high computational loads, complex background clutter, and nonstationary sensors. Active sensors, such as ultrasonic, radar, and laser, are expensive and can pose interference problems in heavy traffic. Thus, this paper explores the possibility of a...
Detecting humans from video sequences is a key and difficult problem in computer vision. The problem becomes even more challenging when the camera is mobile, and thus, the background is not static. In this case, the traditional approach of background subtraction cannot be employed. Methods based on Histogram of Oriented Gradients (HOG) have been introduced and widely used for human detection due to...
This work obtains shaking-free route panoramas from a vehicle borne camera. We detect the shaking and waving profile of the camera and rectify a long route panorama automatically for an ideal parallel-perspective projection. Different from the traditional flow based approach that works on entire frame by point tracking, we create a stationary blurred temporal image called condensed slice during the...
In recent years, some computer vision algorithms such as SIFT (scale invariant feature transform) have been employed in image similarity match to perform image-based search applications. However, with the increasing scale of image databases, centralized image retrieval system no longer provide adequate prompt search. In this paper, we design a scalable distributed architecture, which is analog to...
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