Survey on Multiple Objects Tracking in Video Analytics

Parihar, Anjali; Nagarkar, Priyanka; Bhosale, Vishakha; Desale, Ketan Sanjay

doi:10.5120/ijca2019918292

Cited by 1 publication

(1 citation statement)

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“…Aftab Alam et al provided a review on video big data analytics in the cloud and proposed a service-oriented architecture bridging the gap among large-scale video analytics challenges, big data solutions, and cloud computing [28]. Anjali et al (2019) conducted a survey on multiple object tracking for fast and parallel video processing in MapReduce with the Amazon EC2 Cloud [29]. The results showed that for a large number of videos, the computational speed is faster and the performance is higher when using a fully parallel technique in comparison to a partially parallel technique.…”

Section: State Of the Art Overviewmentioning

confidence: 99%

Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning

Phan

Cao

et al. 2022

Applied Sciences

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In the era of digital media, the rapidly increasing volume and complexity of multimedia data cause many problems in storing, processing, and querying information in a reasonable time. Feature extraction and processing time play an extremely important role in large-scale video retrieval systems and currently receive much attention from researchers. We, therefore, propose an efficient approach to feature extraction on big video datasets using deep learning techniques. It focuses on the main features, including subtitles, speeches, and objects in video frames, by using a combination of three techniques: optical character recognition (OCR), automatic speech recognition (ASR), and object identification with deep learning techniques. We provide three network models developed from networks of Faster R-CNN ResNet, Faster R-CNN Inception ResNet V2, and Single Shot Detector MobileNet V2. The approach is implemented in Spark, the next-generation parallel and distributed computing environment, which reduces the time and space costs of the feature extraction process. Experimental results show that our proposal achieves an accuracy of 96% and a processing time reduction of 50%. This demonstrates the feasibility of the approach for content-based video retrieval systems in a big data context.

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Section: State Of the Art Overviewmentioning

confidence: 99%