![]() In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2012)Īppiah, K., Meng, H., Hunter, A., Dickinson, P.: Binary histogram based split/merge object detection using FPGAs. Stein, F.: The challenge of putting vision algorithms into a car. In: IET Intelligent Transport Systems (2015) Nieto, M., Otaegui, O., Vélez, G., Ortega, J.D., Cortés, A.: On creating vision-based advanced driver assistance systems. In: 2015 Annual IEEE India Conference (INDICON), pp. Gajbhiye, S.D., Gundewar, P.P.: A real-time color-based object tracking and occlusion handling using arm cortex-a7. In: 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. Pea-Gonzlez, R.H., Nuo-Maganda, M.A.: Computer vision based real-time vehicle tracking and classification system. In: 2016 IEEE International Conference on Advances in Electronics, ICAECCT, Communication and Computer Technology, p. Uke, N.J., Futane, P.R.: Efficient method for detecting and tracking moving objects in video. In: 2011 World Congress on Information and Communication Technologies, pp. Prasad, S., Sinha, S.: Real-time object detection and tracking in an unknown environment. In: 2014 Seventh International Symposium on Computational Intelligence and Design (2014) (2016)įirmanda, D., Pramadihanto, D.: Computer vision based analysis for cursor control using object tracking and color detection. Tang, J.W., Shaikh-Husin, N., Sheikh, U.U., Marsono, M.N.: FPGA-based real-time moving target detection system for unmanned aerial vehicle application. (eds.) Advances in Neurotechnology, Electronics and Informatics, Chap. elegans nematode nervous system using high performance FPGAS. In: 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), pp. Guennouni, S., Ahaitouf, A., Mansouri, A.: Multiple object detection using openCV on an embedded platform. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. ![]() In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming).
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