2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSMR-2.12
Paper Title REAL-TIME OBJECT DETECTION AND LOCALIZATION IN COMPRESSIVE SENSED VIDEO
Authors Yeshwanth Ravi Theja Bethi, Sathyaprakash Narayanan, Indian Institute of Science, Banglore, India; Venkat Rangan, tinyVision.ai Inc., United States; Anirban Chakraborty, Chetan Singh Thakur, Indian Institute of Science, Banglore, India
SessionSMR-2: Perception and Quality Models
LocationArea F
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Image and Video Sensing, Modeling, and Representation: Perception and quality models for images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Typically a 1-2MP CCTV camera generates around 7-12GBof 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 signal processing results by reducing the sampling bandwidth. Different sampling mechanisms were developed to incorporate compressive sensing in image and video acquisition. Though all-CMOS sensor cameras that perform compressive sensing save a lot of bandwidth on sampling and minimize the memory required to store videos. However, traditional signal processing and deep learning model can realize operations only on the reconstructed data. The reconstruction of compressive-sensed videos is computationally expensive and time-consuming. In this work, we propose a sparse deep learning model to overcome this overhead to detect and localize the objects directly on the compressed frames. Thus, mitigating the need to reconstruct the frames and reducing the search rate up to 20 times (compression rate). We achieved an accuracy of 46.27% mAP with the proposed model on GeForce GTX 1080 Ti. We were also able to show real-time inference on an NVIDIA TX2 embedded board with 45.11%mAP.