DETIC-TRACK: ROBUST DETECTION AND TRACKING OF OBJECTS IN VIDEO

被引：0

作者：

Fassold, Hannes ^{[1
]}

机构：

[1] JOANNEUM Res DIGITAL, Graz, Austria

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (IEEE ICMEW 2022) | 2022年

关键词：

object detection; tracking; optical flow; deep learning;

D O I：

10.1109/ICMEW56448.2022.9859437

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The automatic detection and tracking of objects in a video is crucial for many video understanding tasks. We propose a novel deep learning based algorithm for object detection and tracking, which is able to detect more than 1,000 object classes and tracks them robustly, even for challenging content. The robustness of the tracking is due to the usage of optical flow information. Additionally, we utilize only the part of the bounding box corresponding to the object shape for the tracking.

引用

页数：1

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