Journal ArticleDOI
A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking
TLDR
Experimental results show that this framework yields a robust efficient on-board vehicle recognition and tracking system with high precision, high recall, and good localization.Abstract:
This paper introduces a general active-learning framework for robust on-road vehicle recognition and tracking. This framework takes a novel active-learning approach to building vehicle-recognition and tracking systems. A passively trained recognition system is built using conventional supervised learning. Using the query and archiving interface for active learning (QUAIL), the passively trained vehicle-recognition system is evaluated on an independent real-world data set, and informative samples are queried and archived to perform selective sampling. A second round of learning is then performed to build an active-learning-based vehicle recognizer. Particle filter tracking is integrated to build a complete multiple-vehicle tracking system. The active-learning-based vehicle-recognition and tracking (ALVeRT) system has been thoroughly evaluated on static images and roadway video data captured in a variety of traffic, illumination, and weather conditions. Experimental results show that this framework yields a robust efficient on-board vehicle recognition and tracking system with high precision, high recall, and good localization.read more
Citations
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Journal ArticleDOI
Data-Driven Intelligent Transportation Systems: A Survey
TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
Journal ArticleDOI
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis
TL;DR: This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding, and discusses the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on- road behavior.
Journal ArticleDOI
Vehicle Detection Techniques for Collision Avoidance Systems: A Review
TL;DR: A comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs).
Journal ArticleDOI
Vehicle Detection and Tracking in Car Video Based on Motion Model
TL;DR: The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically, and the approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.
Journal ArticleDOI
Overview of Environment Perception for Intelligent Vehicles
TL;DR: The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons.
References
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Journal ArticleDOI
Improving Generalization with Active Learning
TL;DR: A formalism for active concept learning called selective sampling is described and it is shown how it may be approximately implemented by a neural network.