Vision-Based Front Vehicle Detection and Its Distance Estimation

Kim

International Journal of Advanced Robotic Systems

2013

In this paper, we present a tracking-by-multiple hypotheses framework to detect and track multiple vehicles accurately and precisely. The tracking-bymultiple hypotheses framework consists of obstacle detection, vehicle recognition, visual tracking, global position tracking, data association and particle filtering. The multiple hypotheses are from obstacle detection, vehicle recognition and visual tracking. The obstacle detection detects all the obstacles on the road. The vehicle recognition classifies the detected obstacles as vehicles or non-vehicles. 3D feature-based visual tracking estimates the current target state using the previous target state. The multiple hypotheses should be linked to corresponding tracks to update the target state. The hierarchical data association method assigns multiple tracks to the correct hypotheses with multiple similarity functions. In the particle filter framework, the target state is updated using the Gaussian motion model and the observation model with associated multiple hypotheses. The experimental results demonstrate that the proposed method enhances the accuracy and precision of the region of interest.

Section: Introductionmentioning

confidence: 99%

Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking

Kim

International Journal of Advanced Robotic Systems

2013

“…If drivers faced with emergency driving situations have reliable information provided, the number of car accidents could be reduced dramatically. Such information requires various sensors such as Radar [1], Lidar [2], cameras [3], and GPS to perceive aspects of the external environment. Actually, Team Tartan Racing's vehicle "Boss", Carnegie Mellon University's winning entry in the 2007 DARPA Urban Challenge, is equipped with 13 different perception sensors [4].…”

Section: Introductionmentioning

confidence: 99%

MCMC particle filter-based vehicle tracking method using multiple hypotheses and appearance model

Kim

2013 IEEE Intelligent Vehicles Symposium (IV)

2013

In this study, we propose a multiple vehicle tracking method using multiple hypotheses and the appearance model. The multiple hypotheses are associated with multiple tracks using track-to-multiple hypotheses association method. A target state is estimated using the maximum a posteriori probability estimation method. The posterior probability is proportional to the product of a priori probability and the likelihood that is calculated using similarities of multiple hypotheses and the appearance model. The posterior probability density function is estimated using the Markov chain Monte Carlo particle filter. An optimal posterior target state is determined using a sample with the maximum a posteriori probability. Our experimental results show that the proposed method can improve multiple objects tracking precision as well as multiple object tracking accuracy.

“…As a matter of course, a vision system can also estimate the distance of an object for itself. One method is to utilize perspective information of a road or an object size [5][6][7]. However, it needs a priori information such as a road status or the true size of the target object.…”

Section: Introductionmentioning

confidence: 99%

Distance estimation algorithm for both long and short ranges based on stereo vision system

2008 IEEE Intelligent Vehicles Symposium

Kwon

et al. 2008

We present a distance measurement method based on stereo vision system while guaranteeing accuracy and reliability. It has been considered as difficult problem to measure both long and short distance with a stereo vision system accurately due to sampling error and camera sensor error. To resolve these problems of the stereo vision system, we utilize an algorithm which is consisted of a modified sub-pixel displacement method to enhance the accuracy of disparity and Strong Tracking Kalman filter (STKF) to reduce the camera sensor errors. Our displacement method and the usefulness of STKF are verified as compared to other displacement methods and Conventional Kalman filter (CKF) through simulating on the several distance ranges. The Monte-Carlo simulation results show that our algorithm is capable of measuring up to hundreds of meters while root mean square error (RMSE) maintains about 0.04 at all ranges, even though the target vehicle maneuvers or moves nonlinearly.