Video object detection is the task of detecting objects from a video as opposed to images.
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Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors.
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Models and examples built with TensorFlow
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
In this paper, we present a light weight network architecture for video object detection on mobiles.
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
#3 best model for Video Object Detection on ImageNet VID