We consider the problem of detecting objects, as they come into view, from videos in an
online fashion. We provide the first real-time solution that is guaranteed to minimize the …

Detecting objects in a video is a compute-intensive task. In this paper we propose CaTDet, a
system to speedup object detection by leveraging the temporal correlation in video. CaTDet …

Average precision (AP) is a widely used metric to evaluate detection accuracy of image and
video object detectors. In this paper, we analyze the object detection from video and point …

While single-image object detectors can be naively applied to videos in a frame-by-frame
fashion, the prediction is often temporally inconsistent. Moreover, the computation can be …

This paper introduces an online model for object detection in videos with real-time
performance on mobile and embedded devices. Our approach combines fast single-image …

Successive frames of a video are highly redundant, and the most popular object detection
methods do not take advantage of this fact. Using multiple consecutive frames can improve …

Given the vast amounts of video available online, and recent breakthroughs in object
detection with static images, object detection in video offers a promising new frontier …

Detection-driven real-time video analytics require continuous detection of objects contained
in the video frames using deep learning models like YOLOV3, EfficientDet, etc. However …

Abstract We propose a Spatiotemporal Sampling Network (STSN) that uses deformable
convolutions across time for object detection in videos. Our STSN performs object detection …

Transferring image-based object detectors to the domain of video remains challenging
under resource constraints. Previous efforts utilised optical flow to allow unchanged features …