Dynamic Scene Recognition Using Spatiotemporal Based DLTP on Spark

Uddin, Azher; Akhond, Mostafijur Rahman; Lee, Young-Koo

doi:10.1109/access.2018.2878865

Cited by 9 publications

(17 citation statements)

References 39 publications

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“…However, their work does not provide any low-level video processing APIs. Furthermore, the existing literature also lacks support for dynamic feature extraction on the distributed environment, which is a very essential component to provide in any high-level services [10,16]. In order to resolve the above-mentioned issues, in this work, we introduce a distributed video processing library that provides distributed video processing on top of Spark.…”

Section: Distributed Video Data Processing Layermentioning

confidence: 99%

“…While Distributed Feature Extractor is in charge of extracting the predominant features from the videos in a distributed manner. The component of Distributed Feature Extractor includes color feature extractor, dynamic and frame based texture feature extractor, dynamic and frame based shape feature extractor, motion feature extractor, object feature extraction and Key Frame extractor [10,16]. The pre-processing and Feature Extraction are employed based on the selection of higher-level services.…”

Section: Distributed Video Data Processing Layermentioning

confidence: 99%

“…The pre-processing and Feature Extraction are employed based on the selection of higher-level services. The output of the Feature Extractor is represented either as Bag-of-Words [36] or Histogram [10,16] and stored in low-level result DS in the Big Data Curation Layer. Figure 3 demonstrates the architectural overview to produce higher-level services using our proposed low-level distributed video processing APIs.…”

Section: Distributed Video Data Processing Layermentioning

confidence: 99%

“…Our wrapper APIs permit us to achieve most video processing operations in parallel in a distributed environment, which reduces the processing time dramatically. Similar to our previous work [10,16], in this work, we have implemented several video processing algorithms on top of Spark that includes edge detection, video encoding, background subtraction, key-frame extraction and dynamic feature extraction. For distributed dynamic feature extraction, we have built APIs for Volume Local Binary Pattern (VLBP) [37], Volume Local Ternary Pattern (VLTP), Local Binary Pattern for three orthogonal planes (LBP-TOP) [37] and Directional Local Ternary Pattern for three orthogonal planes (DLTP-TOP) [16].…”

Section: Distributed Video Data Processing Layermentioning

confidence: 99%

“…Besides, in References [14,15], the authors introduced a Streaming Video Engine (SVE) for uploading and processing videos in a distributed manner and a framework for real-time video analysis on Spark, respectively. Despite that, existing literature lacks support for the dynamic feature extraction on a distributed environment, which is a very essential component to provide any high-level services [10,16]. To address this issue, we introduce a distributed video processing library that provides video processing on top of Spark.…”

Section: Introductionmentioning

confidence: 99%

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SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Uddin

Alam

et al. 2019

Symmetry

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In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

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Section: Distributed Video Data Processing Layermentioning

confidence: 99%

Section: Distributed Video Data Processing Layermentioning

confidence: 99%