Abstract
The trade-off between spatial and temporal resolution remains a fundamental challenge in machine vision. A captured image often contains a significant amount of redundant information, and only a small region of interest (ROI) is necessary for object detection and tracking. In this paper, we first systematically characterize the effects of ROI on camera capturing, data transmission, and image processing. We then present the closed-loop ROI algorithm capable of high spatial and temporal resolution as well as wide scanning field of view (FOV) in single and multi-object detection and tracking via real-time wireless video streaming. With the feedback from real-time object tracking, the wireless camera is able to capture and transmit only the ROI which in turn enhances both the spatial and temporal resolution in object tracking. In addition, the proposed approach can still maintain a large FOV by processing regions outside of the ROI at lower spatial and temporal resolutions. When applied to a high spatial resolution wireless stream (5 MegaPixels), the closed-loop ROI algorithm improves the temporal resolution by up to 10× (from 2.4FPS to 22.5FPS). Specifically, camera processing is improved by up to 4.7×, data transmission is improved by up to 160×, and PC processing is improved by up to 2.5×. In a person tracking experiment, the closed-loop ROI algorithm enables a wide-angle camera to outperform both a normal wide-angle camera-which suffers from poor temporal resolution and motion blur-and a pan & tilt camera-which cannot automatically refresh tracking after the tracking is lost.
Highlights
A fundamental trade-off between spatial and temporal resolution remains a key challenge in machine vision
Higher spatial resolution frames require more time to capture, process, and transmit, resulting in lower temporal resolution. Both high spatial and high temporal resolutions may be desirable as they inform object detection and tracking accuracy
Spatial resolution can affect the accuracy of neural networks [1], [2], and even small changes can have a significant effect depending on the application [3]
Summary
A fundamental trade-off between spatial and temporal resolution remains a key challenge in machine vision. Higher spatial resolution frames require more time to capture, process, and transmit, resulting in lower temporal resolution. Both high spatial and high temporal resolutions may be desirable as they inform object detection and tracking accuracy. Spatial resolution can affect the accuracy of neural networks [1], [2], and even small changes can have a significant effect depending on the application [3]. Temporal resolution can affect the accuracy of object detection and object tracking [4]–[6]. There is often a tradeoff between spatial and temporal resolutions [7], [8]
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