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Object detection is crucial in video analytics pipelines, but there is a need to optimize deep neural networks (DNNs)-based object detection for resource-constrained Internet of Things (IoT) devices. The computational constraints inherent to the IoT device inevitably curtail its precision and real-time efficacy in the domain of object detection, with pronounced challenges arising, particularly when confronted with high-resolution video streams. To overcome these limitations, we propose using physical dynamics (UPD), a novel on-device system that enables real-time and accurate object detection for high-resolution video streams. UPD employs a lightweight tracking algorithm for the detection of the majority of video frames, concurrently executing the object detector in a parallel fashion only in select instances. UPD addresses tracking errors by eliminating inaccurate feature points and correcting tracking results using …