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A Computer Vision Sensor for AI Accelerated Detection and Tracking of Occluded Objects
  • Can Cuhadar,
  • Hoi Nok Tsao
Can Cuhadar
Nanyang Technological University

Corresponding Author:[email protected]

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Hoi Nok Tsao
Nanyang Technological University
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A prominent problem in computer vision is occlusion, which occurs when an object’s key features temporarily disappear behind another crossing body, causing the computer to struggle with image detection. While the human brain is capable of compensating for the invisible parts of the blocked object, computers lack such scene interpretation skills. Cloud computing using convolutional neural networks is typically the method of choice for handling such a scenario. However, for mobile applications where energy consumption and computational costs are critical, cloud computing should be minimized. In this regard, we propose a computer vision sensor capable of efficiently detecting and tracking covered objects without heavy reliance on occlusion handling software. Our edge-computing sensor accomplishes this task by self-learning the object prior to the moment of occlusion and uses this information to “reconstruct” the blocked invisible features. Furthermore, the sensor is capable of tracking a moving object by predicting the path it will most likely take while travelling out of sight behind an obstructing body. Finally, sensor operation is demonstrated by exposing the device to various simulated occlusion events. 
Keywords:  Computer vision, occlusion handling, edge computing, object tracking, dye sensitized solar cell.
Corresponding author Email: [email protected]  
10 Jan 2022Submitted to AISY Interactive Papers
10 Jan 2022Published in AISY Interactive Papers
Nov 2022Published in Advanced Intelligent Systems volume 4 issue 11 on pages 2100285. 10.1002/aisy.202100285