REAL-TIME OBJECT DETECTION AND LOCALIZATION IN COMPRESSIVE SENSED VIDEO

Typically a 1-2MP CCTV camera generates around 7-12GB of data per day. Frame-by-frame processing of such an enormous amount of data requires hefty computational resources. In recent years, compressive sensing approaches have shown impressive compression results by reducing the sampling bandwidth. Different sampling mechanisms were developed to incorporate compressive sensing in image and video acquisition. Though all-CMOS [1, 2] sensor cameras that perform compressive sensing can help save a lot of bandwidth on sampling and minimize the memory required to store videos, the traditional signal processing, and deep learning models can realize operations only on the reconstructed data. To realize the original uncompressed domain, most reconstruction techniques are computationally expensive and time-consuming. To bridge this gap, we propose a novel task of detection and localization of objects directly on the compressed frames. Thereby mitigating the need to reconstruct the frames and reducing the search rate up to 20× (compression rate). We achieved an accuracy of 46.27% mAP with the proposed model on a GeForce GTX 1080 Ti. We were also able to show real-time inference on an NVIDIA TX2 embedded board with 45.11% mAP, thereby achieving the best balance between the accuracy, inference time, and memory constraints.