Real-time multiple vehicle detection and tracking from a moving vehicle
By Margrit Betke, Esin Haritaoglu, Larry S. Davis. Machine Vision and Applications, 2000
342 citations recorded by the source corpus.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/864a7068c346ecbc4ef6c4da66e4c8bcc83fe560
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Likun Xia
- Amir Mukhtar
- T. Tang
- Zehang Sun
- George Bebis
- Ronald Miller
- Kostas Kalaitzakis
- V. Kastrinaki
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaogang Wang
- Ming-Hsuan Yang
- Dacheng Tao
- Bernt Schiele
- Jitendra Malik
- Yi Yang
- Trevor Darrell
Citations in the corpus
- Vehicle Detection Techniques for Collision Avoidance Systems: A Review (2015, methodology): “Use of horizontal and vertical edges as features for HG is perhaps the most favorable method for practical perspective [83], [96] because of the rapid hardware implementations.”
- A survey of video processing techniques for traffic applications (2003, methodology): “[14] for road boundaries and lane markings, with their parameters estimated via a recursive least squares (RLS) filter fit on candidate edge points.”
- Multiple object tracking: A literature review (2014, background): “Objects to track can be, for example, pedestrians on the street [1], [2], vehicles in the road [3], [4], sport players on the court [5], [6], [7], or groups of animals (birds [8], bats [9], ants [10], fish [11], [12], [13], cells [14], [15], etc.”
- Object Detection and Tracking for Autonomous Navigation in Dynamic Environments (2010, background): “Remarkable progress has been made in highway traffic situations (Betke et al. 2000) and other largely pedestrian-free scenarios such as the DARPA Urban Challenge (DARPA 2008).”
- The Camera Mouse: visual tracking of body features to provide computer access for people with severe disabilities (2002, background): “The assumption that corresponding brightness patterns in subsequent frames are constant, the " constant brightness assumption, " is often made when designing algorithms for motion analysis in images [24], [25].”
- On-road vehicle detection: a review (2006, methodology): “[46] utilized edge information to detect distant cars.”