Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets

Jana, Pulak Kumar; Bhaumik, Swarnabja; Mohanta, Partha Pratim

doi:10.1109/tencon54134.2021.9707226

Cited by 2 publications

(2 citation statements)

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“…As such, we have reached a stage where researchers are moving on to improve performance on even complex computer vision problems, viz. 3D object detection [32], action detection and localization [3] tracking objects across videos, event recognition and scene understanding [33][4], etc. In this chapter, we focus on the task of event/activity recognition in videos, done with the assistance of frame-wise object detection, which enables inter-frame tracking of objects.…”

Section: Video Activity Recognition Assisted By Object Detectionmentioning

confidence: 99%

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Recent Trends in 2D Object Detection and Applications in Video Event Recognition

Jana¹,

Mohanta²

2022

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Object detection serves as a significant step in improving performance of complex downstream computer vision tasks. It has been extensively studied for many years now and current state-of-the-art 2D object detection techniques proffer superlative results even in complex images. In this chapter, we discuss the geometry-based pioneering works in object detection, followed by the recent breakthroughs that employ deep learning. Some of these use a monolithic architecture that takes a RGB image as input and passes it to a feed-forward ConvNet or vision Transformer. These methods, thereby predict class-probability and bounding-box coordinates, all in a single unified pipeline. Two-stage architectures on the other hand, first generate region proposals and then feed it to a CNN to extract features and predict object category and boundingbox. We also elaborate upon the applications of object detection in video event recognition, to achieve better fine-grained video classification performance. Further, we highlight recent datasets for 2D object detection both in images and videos, and present a comparative performance summary of various state-of-the-art object detection techniques.

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Section: Video Activity Recognition Assisted By Object Detectionmentioning

confidence: 99%

“…The task of video event recognition [2] is to predict the ongoing event or activity in a video throughout its duration. The prevalent approach is to obtain a global video-level feature representation [3] from 3D CNNs and classify the video using the same. However, this is generally insufficient for fine-grained tasks.…”

Section: Introductionmentioning

confidence: 99%