A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking

By Sayanan Sivaraman, Mohan Manubhai Trivedi. IEEE transactions on intelligent transportation systems (Print), 2010

386 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c549a421d644aed8694bcb3cc7be7db00dc2b8bf

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Citations in the corpus

Vehicle Detection Techniques for Collision Avoidance Systems: A Review (2015, background): “It serves two major purposes: estimation of motion and position of vehicles; maintaining awareness of previously detected vehicles that were not detected in a given frame [167], while filtering out spurious false positives [168].”
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis (2013, methodology): “Haar-like features have been extensively used to detect the rear faces of preceding vehicles, using a forward-facing camera [37], [41]– [49].”
Vehicle Detection and Tracking in Car Video Based on Motion Model (2011, background): “Several works have employed probability in their formulation, but they are mainly applied to the occurrence and appearance (intensity distribution) of vehicles [2], [11], [15], [16], [26], or some of them are from a static camera [17]–[19].”
Overview of Environment Perception for Intelligent Vehicles (2017, background): “,2009 [153] Haar-like features Particle filter • Dataset: LISA-Q Front FOV data set • Performance evaluate:”
Virtual and Real World Adaptation for Pedestrian Detection (2014, background): “Index Terms—Pedestrian detection, photo-realistic computer animation, data set shift, domain adaptation Ç”
Data-Driven Intelligent Transportation Systems: A Survey (2011, background): “McCall and Trivedi [44] gave a comprehensive survey on the detection and tracking from five aspects, including road modeling, road marking extraction, preprocessing, vehicle modeling, position tracking, and common assumptions and comparative analysis.”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “The purpose of changing the DBN is that we need to include the latest measurement into the motion estimate, which is suitable for using SSPF because as a global optimizer, the SSPF can directly solve high-dimensional, non-linear, and multi-mode motion estimate.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “…detection include symmetry [115]– [118], edge [119]– [121], headlight/taillight (for nighttime) [122], [123], histogram of oriented gradient (HOG) features [124]– [126], Haar-like features [127]– [131], scale invariant feature transform (SIFT) features [132], [133] and Gabor features [129], [134].”