Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

/pdf/on-road-vehicle-detection-a-review-4iw469j6iz.pdf

On-road vehicle detection: a review

The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.

/pdf/computer-vision-based-fabric-defect-detection-a-survey-27mr8b2pf3.pdf

Computer-Vision-Based Fabric Defect Detection: A Survey

In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.

Crowd analysis: a survey

The effectiveness of probabilistic tracking of objects in image sequences has been revolutionized by the development of particle filtering. Whereas Kalman filters are restricted to Gaussian distributions, particle filters can propagate more general distributions, albeit only approximately. This is of particular benefit in visual tracking because of the inherent ambiguity of the visual world that stems from its richness and complexity. One important advantage of the particle filtering framework is that it allows the information from different measurement sources to be fused in a principled manner. Although this fact has been acknowledged before, it has not been fully exploited within a visual tracking context. Here we introduce generic importance sampling mechanisms for data fusion and discuss them for fusing color with either stereo sound, for teleconferencing, or with motion, for surveillance with a still camera. We show how each of the three cues can be modeled by an appropriate data likelihood function, and how the intermittent cues (sound or motion) are best handled by generating proposal distributions from their likelihood functions. Finally, the effective fusion of the cues by particle filtering is demonstrated on real teleconference and surveillance data.

/pdf/data-fusion-for-visual-tracking-with-particles-3rjpt37twm.pdf

Data fusion for visual tracking with particles

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple people. The experiments furthermore show that the approach outperforms other techniques developed so far.

http://pages.iai.uni-bonn.de/schulz_dirk/articles/people-tracking-ijrr-03.pdf

People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters

Tracking multiple objects using the Condensation algorithm

http://bigwww.epfl.ch/publications/unser8403.pdf

Feature extraction and decision procedure for automated inspection of textured materials

In this paper, the authors present a driver support system that is based on coarse range image sequences This new method for the segmentation and tracking of obstacles, is able to automatically keep the vehicle at an adequate distance or warn the driver when the distance is too close to other vehicles The authors describe the range sensor, the test data, the separation of image features tracking process, and results on range images Results from experiments with simulated traffic sense indicate that the system is robust

Object detection and tracking in range image sequences by separation of image features

Grouping Symmetrical Structures for Object Segmentation and Description

A new method for the inspection of textile webs is proposed. The normal web structure is characterized by the micro-texture of 3x3 neighbourhoods which is extracted by principal components. Local 'rectification' of principal component images yields feature planes which can be fed to a classificator. Preliminary results are presented.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Frank Ade

Papers

Tracking multiple objects using the Condensation algorithm

Feature extraction and decision procedure for automated inspection of textured materials

Object detection and tracking in range image sequences by separation of image features

Grouping Symmetrical Structures for Object Segmentation and Description

Application Of Principal Component Analysis To The Inspection Of Industrial Goods