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@article{182166,
        author = {Thenmozhi R and Manjupriya R and Shenbaga Vadivu},
        title = {Resilient Video Streaming Analytics for Traffic Surveillance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1085-1091},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182166},
        abstract = {server Traffic surveillance is an important issue for Intelligent Transportation systems (ITS) that helps to detect incidents automatically, such as wrong-way drivers, still-standing vehicles, and traffic jams. Traffic surveillance system requires a fast and short-term deployment of video cameras on the roads. In traffic surveillance, ad-hoc networks could be a low-cost and feasible option, but they have poor performance for video delivery. We propose a smart live video adaptive streaming technique to be employed in transportation for the video streams captured from the cameras to the external road Traffic Monitoring System (TMS) servers in a more efficient way. To achieve this goal, the proposed system analyzes video quality under their inherent constraints, given by the node's connectivity topologies, the inbuilt video streaming protocol, node's performance, and routing protocols. The methodology consists of two stages. In the initial stage, the identification of proper network is considered to transmit the road traffic video in an adaptive way using a reliable transport protocol which is Transmission Control Protocol (TCP). This. On further analysis, it has been found that among the available streaming alternatives, Dynamic Adaptive Streaming over HTTP (DASH) exhibits the best results over the other video streaming techniques. In the second stage, a complete analysis was carried out on multi-hop networks under the scenarios used for TMS based on the specific topologies required, with DSR routing protocol. The behavior of the received video is studied with relative merits in terms of throughput, delivery ratio, routing overhead, and delay. The experimentation motivates configuring more efficient routing protocols and streaming video techniques. It is also concluded that multi-hop networks and routing protocols will enhance QoS metrics and improve the overall network performance. The proposed video steaming analytics helps understand the behavior of on-road vehicles. It explores the possibility of significant time and space to avoid accidents in micro variations of space and time.},
        keywords = {TMS – Traffic Monitoring Systems, Traffic surveillance, ITS - Intelligent Transportation Systems, Adaptive live video streaming, multi-hop network, DASH-dynamic adaptive streaming over HTTP},
        month = {July},
        }