Approximate reasoning and finite state machines to the detection of actions in video sequences

Rodríguez-Benítez, Luis; Solana-Cipres, C.; Moreno‐Garcia, Juan; Jiménez-Linares, Luis

doi:10.1016/j.ijar.2010.12.003

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…TRECVID [54] is a benchmark for evaluating content-based video event retrieval and measure their performance. Activity recognition [55], is another domain which involves detection of predefined human actions like walking, jumping, and cooking. Video analytics frameworks like NoScope [56] and FOCUS [57] provide low cost and low latency video event detection on indexed video dataset.…”

Section: B Event Matching In Video Streamsmentioning

confidence: 99%

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Yadav

Curry

2019

2019 IEEE International Conference on Big Data (Big Data)

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Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have inherent limitations to query video streams due to their unstructured data model and lack of expressive query language. In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns. In this context, we propose-i) VidCEP, an in-memory, on the fly, near real-time complex event matching framework for video streams. The system uses a graphbased event representation for video streams which enables the detection of high-level semantic concepts from video using cascades of Deep Neural Network models, ii) a Video Event Query language (VEQL) to express high-level user queries for video streams in CEP, iii) a complex event matcher to detect spatiotemporal video event patterns by matching expressive user queries over video data. The proposed approach detects spatiotemporal video event patterns with an F-score ranging from 0.66 to 0.89. VidCEP maintains near real-time performance with an average throughput of 70 frames per second for 5 parallel videos with sub-second matching latency.

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Section: B Event Matching In Video Streamsmentioning

confidence: 99%

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Yadav

Curry

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…However, FSM may easily fail in the presence of noise. It is applied as recognizer in various methods [15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition from Basic Actions Using Finite State Machine

Noorit

Suvonvorn

2013

Lecture Notes in Electrical Engineering

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High-level human activity recognition is an important method for the automatic event detection and recognition application, such as, surveillance system and patient monitoring system. In this paper, we propose a human activity recognition method based on FSM model. The basic actions with their properties for each person in the interested area are extracted and calculated. The action stream with related features (movement, referenced location) is recognized using the predefined FSM recognizer modeling based on rational activity. Our experimental result shows a good recognition accuracy (86.96% in average).

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Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Kaushal

Khehra²,

Sharma

2018

Applied Soft Computing

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Approximate reasoning and finite state machines to the detection of actions in video sequences

Cited by 5 publications

References 23 publications

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Human Activity Recognition from Basic Actions Using Finite State Machine

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

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