A smart moving vehicle detection system using motion vectors and generic line features

Wong, Chup-Chung; Siu, Wan-Chi; Jennings, Paul; Barnes, Stuart J.; Fong, B.

doi:10.1109/tce.2015.7298299

Cited by 19 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the other related consumer applications in a vehicle, Wong et al [25] proposed moving vehicle detection combined with motion vectors and generic line features for an ADAS.…”

Section: Figurementioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

View full text Add to dashboard Cite

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergencyrelated announcements is approximately 7 s. INDEX TERMS Advanced driver assistance system (ADAS), artificial intelligence over Internet of Things (AIoT), automotive, deep learning, Internet of Vehicles (IoV), head-on and single-vehicle accident detection.

show abstract

“…For the other related consumer applications in a vehicle, Wong et al [25] proposed moving vehicle detection combined with motion vectors and generic line features for an ADAS.…”

Section: Figurementioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…According to the detected features, a targeted vehicle was marked in the corresponding location of the source image to achieve detection. According to the image edge features, Wong categorised the image into parts, such as background, foreground, and two horizontal sections [9]. For instance, sky and road are background, vehicles are treated as foreground, and the integrated edge information is used as two horizontal sections.…”

Section: Vehicle Visual Perception Reconstructionmentioning

confidence: 99%

Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Wang

Haner

et al. 2019

CAAI Transactions on Intelligence Technology

View full text Add to dashboard Cite

Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.

show abstract

“…Chup-Chung Wong et.al. [16] proposes a method of assistance for advanced driver system by combining the mass produced in vehicle devices and moving object based motion vector. The output of video encoder is readily utilized for motion vectors.…”

Section: Literature Surveymentioning

confidence: 99%

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Patil¹,

N²

2019

Res. Bus.

View full text Add to dashboard Cite

Moving item location in any video scene and its detection are the 2 significant testing errands in the moving video investigation system which is dynamic in nature. It is trying because of impediment, nearness of shadows, jumbling, dynamic foundation, clamor etc. Detection of moving objects, following, object coordinating crosswise over multi-camera, and reidentification are the fundamental strides of multi camera video investigation system. Multiple object location and following in savvy city video investigation can be created by propriety of society, for example, shrewd observation, savvy stopping, traffic observing, vehicle route, savvy human services and so forth. The objective of this paper is to examine and audit different methodologies towards such different types of moving object detection, its segmentation, histogram equalization, filtering, classification etc… Also, this survey paper gives a brief idea about the work done by various authors till date in the field of video image processing so that this review papers serves as a base paper for many of the researchers who want to do research in this exciting field of video processing. Some of the drawbacks or the disadvantages of the works that were carried out by the earlier researchers are considered in our research work, studied in brief & problem is being defined along with the objectives in order to overcome some of the deficiencies of the existing algos.

show abstract

A smart moving vehicle detection system using motion vectors and generic line features

Cited by 19 publications

References 17 publications

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Contact Info

Product

Resources

About