This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

https://cvrr.ucsd.edu/publications/2013/SayananTrivedi_IEEETITS2013_VehicleSurvey.pdf

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

Face detection has been one of the most studied topics in the computer vision literature. In this technical report, we survey the recent advances in face detection for the past decade. The seminal Viola-Jones face detector is first reviewed. We then survey the various techniques according to how they extract features and what learning algorithms are adopted. It is our hope that by reviewing the many existing algorithms, we will see even better algorithms developed to solve this fundamental computer vision problem. 1

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/facedetsurvey.pdf

A Survey of Recent Advances in Face Detection

Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

A rapid learning algorithm for vehicle classification

Event analysis in videos is a critical task in many applications. Activity recognition that aims to recognize actions from video and in particular abnormal event recognition in surveillance video has received significant attention from the research community. In this special issue, we focus on event analysis in broad problem domains. Event recognition in specific domains, such as highlight detection in sports videos, has attracted much interest in the past decade. Recently, due to the emergence of online video search, the research community has become interested in event content analysis for both broadcast and user-generated videos. For news videos, Large-Scale Concept Ontology for Multimedia (LSCOM) has defined 56 event/activity concepts, covering a broad range of events such as airplane flying, car crash, riot, people marching, and so on. Researchers have also started to investigate event recognition from other video sources, such as education videos and medical videos. For these applications, we have witnessed the effectiveness of using both static and temporal information.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY Editor-in-Chief

This paper presents a real-time vision-based vehicle's rear detection system using gradient based methods and Adaboost classification, for ACC applications. Our detection algorithm consists of two main steps: gradient driven hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, possible target locations are hypothesized. This step uses an adaptive range-dependant threshold and symmetry for gradient maxima localization. Appearance-based hypothesis validation verifies those hypothesis using AdaBoost for classification with illumination independent classifiers. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, varying lightening conditions), illustrating good performance.

Vehicle detection combining gradient analysis and AdaBoost classification

We present an improvement to the object detection scheme of Viola and Jones (2004), using the example of face detection. We present a new kind of visual features that is based on sampling individual pixels within the examined sub-window, instead of comparing the sums of pixel values in rectangular regions. Our features are faster and do not demand the preparation of an integral image, nor variance-normalisation of each sub-window. The result is an improved system that detects faces in quarter-PAL images in only 9 milliseconds per image on a 2.4-GHz pentium computer.

Yet Even Faster (YEF) real-time object detection

We present an improvement to the object detection scheme of Viola and Jones [5], using the example of face detection. We present a new kind of visual features, which is based on sampling individual pixels within the examined sub-window, instead of comparing the sums of pixel values in rectangular regions. Our features are faster and do not demand the preparation of an integral image, nor variance-normalization of each sub-window. The result is an improved system which detects faces in quarter-PAL images in only 9 millisecond per image on a 2.4GHz pentium

YEF∗Real-Time Object Detection

/pdf/active-learning-for-visual-object-recognition-4bxy7jfzxf.pdf

Yotam Abramson

Papers

Vehicle detection combining gradient analysis and AdaBoost classification

Yet Even Faster (YEF) real-time object detection

YEF∗Real-Time Object Detection

Active learning for visual object recognition

YEF (Yet Even Faster) Real-Time Object Detection.