Novel Approach of Video Tracking System Using Learning-Based Mechanism over Crowded Environment

Kumar, Karanam Sunil; Kavya, N

doi:10.1007/978-3-030-77442-4_5

Cited by 2 publications

(1 citation statement)

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“…This study continues our prior works in [36], [37]. The formulated system model consists of various subcomputational units as i) video sequence explorer: where the training and testing data are visualized to get an intuition about the scene, ii) Dictionary feature lexicon blocks: where the dictionary based on the blocks are made, iii) Feature engineering modeling with feature element and lexicon vector computation, followed by iv) Design and development of a cost-effective learning model to estimate learning-based features which help in identifying the unusual moving object from each frame of the input video sequence and v) Exploration of the numerical outcome to justify the performance of the proposed modeling.…”

Section: System Modelsupporting

confidence: 83%

An Efficient Unusual Event Tracking in Video Sequence using Block Shift Feature Algorithm

Kumar¹,

Kavya²

2022

IJACSA

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The area of video technology is rapidly growing owing to advancements in intelligent video systems in sensor operations, higher bandwidth capacity, storage, and highresolution displays. This led to the proliferation of video-based computing modeling to perform specific tasks on video sequences to gain more insight from the data. Visual tracking of events is a core component in video visual surveillance systems that classify and track moving objects to describe their behavioral aspects. The prime motive behind intelligent video systems is to perform efficient video analytics to meet the specific requirements of the user/use-cases. It involves a self-directed paradigm to understand event sequences, reducing the computational burden of characterizing the activities. The study incorporates a block-shift feature algorithm and introduces a novel computational research method for unusual event tracking in video sequences. The formulated approach employs a framework combining operational blocks to compute sequential operations such as block-matching from the dictionary of motion estimations. Before applying the learning model, the subsequent analysis procedure adds feature lexicon and dominant attributes to make the execution computationally efficient. Further, it uses a sparsenon negative factorization approach to organize the informative details into k possible finite clusters. The event detection outcome from the training datasets of video sequences shows better experimental results than the traditional highly cited related approach of unusual object detection and tracking.

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Section: System Modelsupporting

confidence: 83%