Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

By Sayanan Sivaraman, Mohan Manubhai Trivedi. IEEE transactions on intelligent transportation systems (Print), 2013

926 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/59b202ccc01bae85a88ad0699da7a8ae6aa50fef

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In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, background): “A comprehensive survey of maneuver-based models can be found in [1], [6].”
Computer vision in automated parking systems: Design, implementation and challenges (2017, background): “Vehicle detection and tracking is often done in the context of front camera detection [19] for applications like auto emergency braking or in trafﬁc surveillance applications [20].”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “The optical flow was also proposed to analyze road scenes [9], [118].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “On the other hand, for the stereo camera, stereo matching, appearance-based methods, and motion-based methods are frequently utilized for vehicle detection [37].”
Using a Deep Reinforcement Learning Agent for Traffic Signal Control (2016, background): “Video cameras [18] are becoming more common as sensor devices at inter-sections and vehicles with wireless communication capabilities (i.e., Connected Vehicles [19]) are expected to be deployed in the near future.”
Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances (2015, background): “Examples of such functions include adaptive cruise control (ACC) [9], automatic emergency braking (AEB) [10], lane keeping (LK) [11], high beam assist (HBA) [12].”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, background): “Further, we exclude device-level signal processing and highlevel intelligence generation (such as vehicle behavior [49]).”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “Vision-based vehicle detection and tracking have been topics of great interest to researchers over the past decade [8] and a variety of PF-based tracking methods have been developed [9], [10], [11], [12], [13], [14], [15].”
Vehicle Detection Techniques for Collision Avoidance Systems: A Review (2015, methodology): “It is also recommend to read other comprehensive reviews [12] and [13] for driver safety systems.”
Depth-Attentional Features for Single-Image Rain Removal (2019, background): “, object detection [17] and tracking [40] for surveillance [2], autonomous driving [20], and driver assistance [33].”
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime (2018, background): “Typical examples include the detection of roads [4], traffic lights [13], cars and pedestrians [6], [25], and tracking of such objects [27], [19].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “For more information on conventional hand-crafted feature/cue based approaches, interested readers may refer to the following survey papers: [68,69] for lane line marking detection, [70] for road surface detection, [71,72] for vehicle detection and [73] for pedestrian detection.”
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “Typical examples include the detection of roads [62], traffic lights [63], cars and pedestrians [64,65,1,2], and the tracking of such objects [66,67,68].”
Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking (2014, background): “I. INTRODUCTION Advanced Driver Assistance Systems (ADAS) and autonomous cars are currently very popular research topics, with many applications, as car manufacturers continue to put efforts into making cars safer and easier to drive.”
A Survey on 3D Object Detection Methods for Autonomous Driving Applications (2019, background): “Such configuration uses matching algorithms to find correspondences in both images and calculate the depth of each point relative to the camera, demanding more processing power [18].”
Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors (2019, background): “…and most of the works were primarily focused on improving detection range and accuracy, as well as reducing computational cost (Premebida et al., 2007; Premebida et al., 2009; Ismail et al., 2009; Sivaraman and Trivedi, 2013; Ai and Tsai, 2016; Chavez-Garcia and Aycard, 2016; Du et al., 2017).”