  We propose an efficient cross-cameras surveillance system called,STAC, that
leverages spatio-temporal associations between multiple cameras to provide
real-time analytics and inference under constrained network environments. STAC
is built using the proposed omni-scale feature learning people reidentification
(reid) algorithm that allows accurate detection, tracking and re-identification
of people across cameras using the spatio-temporal characteristics of video
frames. We integrate STAC with frame filtering and state-of-the-art compression
for streaming technique (that is, ffmpeg libx264 codec) to remove redundant
information from cross-camera frames. This helps in optimizing the cost of
video transmission as well as compute/processing, while maintaining high
accuracy for real-time query inference. The introduction of AICity Challenge
2023 Data [1] by NVIDIA has allowed exploration of systems utilizing
multi-camera people tracking algorithms. We evaluate the performance of STAC
using this dataset to measure the accuracy metrics and inference rate for reid.
Additionally, we quantify the reduction in video streams achieved through frame
filtering and compression using FFmpeg compared to the raw camera streams. For
completeness, we make available our repository to reproduce the results,
available at https://github.com/VolodymyrVakhniuk/CS444_Final_Project.
