Detecting a moving pedestrian is still a challenging task in a smart surveillance system due to dynamic scenes. Locating and detecting the moving pedestrian simultaneously influences the development of an integrated but low-resource smart surveillance system. This paper proposes a novel approach to locating and detecting moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv2, YOLOv3, and YOLOv4 within the provided ROI. Our proposed method resulted in more processing frames per second compared with previous approaches. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. It costs around ~50 fps in CPU to classify moving pedestrians and maintain a highly accurate rate. Due to its fast processing, the proposed approach is suitable for IoT or smart surveillance device which has limited resource.INDEX TERMS moving object analysis; pedestrian localization and detection, convolutional neural network (CNN); integrated surveillance system; YOLO.
Vehicular Ad Hoc Network (VANET) or vehicle network is a technology developed for autonomous vehicles in Intelligent Transportation Systems (ITS). The communication system of VANET is using a wireless network that is potentially being attacked. The Sybil attack is one of the attacks that occur by broadcasting spurious information to the nodes in the network and could cause a crippled network. The Sybil strikes the network by camouflaging themselves as a node and providing false information to nearby nodes. This study is conducted to predict the Sybil attack by analyzing the attack pattern using a deep learning algorithm. The variables exerted in this research are time, location, and traffic density. By implementing a deep learning algorithm enacting the Sybil attack pattern and combining several variables, such as time, position, and traffic density, it reaches 94% of detected Sybil attacks.
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