OBJECT DETECTION AND IDENTIFICATION USING ENHANCED CAMERA/VIDEO IMAGING SYSTEMS (E-C/VISs) ON HEAVY TRUCKS
نویسندگان
چکیده
Tests were performed to determine the feasibility of developing an Enhanced Camera/Video Imaging System (E-C/VIS) to provide heavy vehicle drivers with better situation awareness to the sides and rear of their vehicles. It is well known that large blind spots currently exist in these areas and that sideswipe crashes can occur as a result. An additional goal was to extend the operating envelope of conventional video to nighttime and to inclement weather. A three channel system was envisioned in which there would be a camera at each (front) fender of the tractor looking backward along the sides of the tractor trailer. The third channel would be aimed rearward from the back of the trailer. Indoor tests involved selection of components having the best capabilities, while early outdoor tests used the selected components in a single-channel side mounted system. Once developed, the heavy vehicle three-channel system was tested in a static object detection and identification experiment, as well as a dynamic on-road experiment. The current document describes the static object detection and identification experiment methodology and results. In regard to object detection and identification, objects were correctly detected and identified significantly more often with the E-C/VIS than with mirrors alone. Objects directly behind the heavy vehicle could be detected with the rear wide-angle look-down camera of the EC/VIS whereas such objects could not be detected with conventional side mirrors.
منابع مشابه
بازشناسی انسان در سیستمهای نظارت ویدئویی
People re-identification is one of the most important and fundamental processes in video surveillance systems. The accuracy and efficiency of this task influence the effectiveness of the subsequent processes. Event detection and behavior analysis are instances of such subsequent processes that are classified in semantic levels. In people re-identification, having an image or video of an individ...
متن کاملPerformance of multi camera views’ detection using MPEG-7 visual signature tools
The paper presents experimental results for the detection of related video streams (views) of multi camera system using MPEG−7 visual signature tools. This detection can be used for identification of video streams stored in video repositories which constitute the same multi camera recording set. Two signature tools have been investigated: Image Signature descriptor and Video Signature descripto...
متن کاملFeature-based detection and correction of occlusions and split of video objects
This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in object tracking for surveillance applications. The paper assumes a feature-based model for tracking and is based on the identification of sudden variations of spatio-temporal features of objects to detect occlusions and splits. The detection is followed by a validation stage that uses past...
متن کاملNovel Approach for Moving Human Detection and Tracking in Static Camera Video Sequences
An automatic multiple person detection and tracking technique for static camera movies is proposed in this paper. First, a moving human identification method is provided. It detects the video objects by using a novel temporal differencing based algorithm and some morphological processes. Then, our approach decides which moving objects represent walking persons, by computing some human size para...
متن کاملAsed on G Reen - C Hannel P Hoto R Esponse N on - U Niformity ( G - Prnu )
This paper proposes a simple but yet an effective new method for the problem of digital video camera identification. It is known that after an exposure time of 0.15 seconds, the green channel is the noisiest of the three RGB colour channels [5]. Based on this observation, the digital camera pattern noise reference, which is extracted using only the green channel of the frames and is called Gree...
متن کامل