Hardware-software implementation of vehicle detection and counting using virtual detection lines

In this paper a hardware-software system for vehicle detection and counting at intersections is presented. It has been implemented and evaluated on the heterogeneous Zynq platform. The vehicle detection is based on the concept of virtual detection lines (VDL). Differences in colour, horizontal edges and Census transform results between areas around the VDL for two consecutive frames are used to disclosure particular vehicles. This part of the system is designed in hardware description language and implemented in the reconfigurable resources of the Zynq platform. Information about the vehicle presence, along with the time-spatial image obtained at the VDL are transmitted and processed on the ARM core integrated in the Zynq device. This allows to perform further analysis and eliminate false or multiple detections. The solution has been evaluated on several video sequences recorded in various conditions: sunny day (the occurrence of deep shadows), cloudy day, rain and night-time. The system could be an important element of an intelligent transportation system (ITS) i.e. applied in a smart camera.

[1]  W. Marsden.  I and J , 2012 .

[2]  S. M. Mahbubur Rahman,et al.  Detection and Classification of Vehicles From Video Using Multiple Time-Spatial Images , 2012, IEEE Transactions on Intelligent Transportation Systems.

[3]  Zhan Ma,et al.  Smart Cameras , 2020, Encyclopedia of Biometrics.

[4]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[5]  Mau-Tsuen Yang,et al.  Traffic flow estimation and vehicle-type classification using vision-based spatial-temporal profile analysis , 2013, IET Comput. Vis..

[6]  Donald G. Bailey,et al.  Design for Embedded Image Processing on FPGAs , 2011 .

[7]  Jae Wook Jeon,et al.  FPGA design and implementation of a real-time vehicle detection system , 2012, 2012 12th International Conference on Control, Automation and Systems.

[8]  P. Pawlik,et al.  FPGA-based Road Traffic Videodetector , 2007 .

[9]  Bernhard Rinner,et al.  Robust Traffic State Estimation on Smart Cameras , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[10]  Marek Wojcikowski,et al.  Hardware-Software Implementation of a Sensor Network for City Traffic Monitoring Using the FPGA- and ASIC-Based Sensor Nodes , 2013, J. Signal Process. Syst..

[11]  A. Ghasemi,et al.  A real-time multiple vehicle classification and tracking system with occlusion handling , 2012, 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing.

[12]  N. H. C. Yung,et al.  A Method for Vehicle Count in the Presence of Multiple-Vehicle Occlusions in Traffic Images , 2007, IEEE Transactions on Intelligent Transportation Systems.

[13]  Brad L. Hutchings,et al.  Implementing high-performance, low-power FPGA-based optical flow accelerators in C , 2013, 2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors.

[14]  Zbigniew Mikrut,et al.  Video Detection Algorithm Using an Optical Flow Calculation Method , 2012, MCSS.

[15]  Jie Cao,et al.  Vehicle Objects Detection of Video Images Based on Gray-Scale Characteristics , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[16]  Marek Wojcikowski,et al.  FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas , 2011 .

[17]  Marek Wojcikowski,et al.  FPGA-Based Real-Time Implementation of Detection Algorithm for Automatic Traffic Surveillance Sensor Network , 2012, J. Signal Process. Syst..

[18]  Scott Fischaber,et al.  OpenCV based road sign recognition on Zynq , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[19]  Donald G. Bailey,et al.  Design for Embedded Image Processing on FPGAs: Bailey/Design for Embedded Image Processing on FPGAs , 2011 .

[20]  Lukás Sekanina,et al.  Image filter evolution on the Xilinx Zynq Platform , 2013, 2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013).

[21]  Martial Hebert,et al.  A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video , 2005, CVPR.

[22]  Jafar Saniie,et al.  Performance evaluation of FPGA based embedded ARM processor for ultrasonic imaging , 2013, 2013 IEEE International Ultrasonics Symposium (IUS).

[23]  Jun-Wei Hsieh,et al.  Vehicle make and model recognition using symmetrical SURF , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.