A Multi-tier Fusion Strategy for Event Classification in Unconstrained Videos

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

doi:10.1007/978-3-030-34872-4_57

Cited by 5 publications

(1 citation statement)

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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%

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

Jana¹,

Mohanta²

2022

Preprint

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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%

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

Jana¹,

Mohanta²

2022

Preprint

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Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems

Bhaumik¹,

Jana

Mohanta

2021

Advances in Science, Technology &Amp; Innovation

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In this chapter, we aim to aid the development of Cyber-Physical Systems (CPS) in automated understanding of events and activities in various applications of videosurveillance. These events are mostly captured by drones, CCTVs or novice and unskilled individuals on low-end devices. Being unconstrained in nature, these videos are immensely challenging due to a number of quality factors. We present an extensive account of the various approaches taken to solve the problem over the years. This ranges from methods as early as Structure from Motion (SFM) based approaches to recent solution frameworks involving deep neural networks. We show that the long-term motion patterns alone play a pivotal role in the task of recognizing an event. Consequently each video is significantly represented by a fixed number of key-frames using a graph-based approach. Only the temporal features are exploited using a hybrid Convolutional Neural Network (CNN) + Recurrent Neural Network (RNN) architecture. The results we obtain are encouraging as they outperform standard temporal CNNs and are at par with those using spatial information along with motion cues. Further exploring multistream models, we conceive a multi-tier fusion strategy for the spatial and temporal wings of a network. A consolidated representation of the respective individual prediction vectors on video and frame levels is obtained

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