Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 17, December 2019

Compressed Video Stream Based Object Detection


Pyeong Kang Kim1, Hyung Heon Kim1, Tae Woo Kim1 And Young Kyun Cha2, 1Innodep.Inc, Korea and 2Korea University, Korea


Nowadays, the need for research on an intelligent video monitoring system is increasing worldwide. Among the object detection methods, the core technology of the intelligent video monitoring system, or object detection using a deep learning-based convolutional neural network, is used widely due to its proven performance. Nonetheless, deep learning-based object detection requires many hardware resources because it decodes the videos to analyze. Therefore, this article suggests an advanced object recognition technique by conducting compressed video stream-based object detection in order to reduce consumption of resources for object detection as well as improve performance and confirms via the performance evaluation that speed and recognition rate improved compared to existing algorithms such as YOLO, SSD, and Faster RCNN.


object detection, convolutional neural network, Inception V4, Motion Vector