Aka-Net: anchor free-based object detection network for surveillance video transmission in the IOT edge computing environment - Sambandam Raju, Preethi; Arumugam Rajendran, Revathi; Mahalingam, Murugan (Pattern analysis and applications : PAA, 2024)
With the growing use of wireless surveillance cameras in (Internet of things) IoT applications the need to address storage capacity and transmission bandwidth challenges becomes crucial. The majority of successive frames from surveillance cameras contain redundant and irrelevant information, leading to increased transmission burden. Existing video pre-processing techniques often focus on reducing the number of frames without considering accuracy and fail to effectively handle both spatial and temporal redundancies simultaneously. To address these issues, an anchor-free key action point network (AKA-Net) is proposed for video pre-processing in the IoT-edge computing environment. The oriented Features from Accelerated Segment Test (FAST) and rotated Binary Robust Independent Elementary Features (BRIEF) (ORB) feature descriptor is employed to remove duplicate frames, leading to more compact and efficient video representation. The AKA-Net's major contributions include its powerful representation capabilities achieved through the bottleneck module in the information-transferring backbone network, which effectively captures multi-scale features. The information-transferring module helps to improve the performance of the object detection algorithm for video pre-processing by fusing the complementary information from different scales. This allows the algorithm to detect objects of different sizes more accurately, making it highly effective for real-time video pre-processing tasks. Then, the key action point selection module that utilizes the self-attention mechanism is introduced to accurately select informative key action points. This enables efficient network transmission with lower bandwidth requirements, while maintaining high accuracy and low latency. It treats every pixel within the feature map as a temporal-spatial point and leverages self-attention to identify and select the most relevant keypoints. Experiments show that the proposed AKA-Net outperforms existing methods in terms of compression ratio of 54.2% and accuracy with a rate of 96.7%. By addressing spatial and temporal redundancies and optimizing key action point selection, AKA-Net offers a significant advancement in video pre-processing for smart surveillance systems, benefiting various IoT applications.