Neural Network Based Framework For Goal Event Detection In Soccer Videos

Framework for rare event detection using Artificial Neural Network based context free grammar

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189164 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8463-8475

Author(s):

Palanivel Srinivasan ◽

Manivannan Doraipandian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Event Detection ◽

Performance Metrics ◽

Rare Events ◽

Rare Event ◽

Video Stream ◽

Context Free Grammar ◽

Artificial Neural ◽

Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Download Full-text

Neural Network Distillation on IoT Platforms for Sound Event Detection

10.21437/interspeech.2019-2394 ◽

2019 ◽

Cited By ~ 3

Author(s):

Gianmarco Cerutti ◽

Rahul Prasad ◽

Alessio Brutti ◽

Elisabetta Farella

Keyword(s):

Neural Network ◽

Event Detection ◽

Sound Event ◽

Iot Platforms ◽

Sound Event Detection

Download Full-text

Automatic Soccer Video Event Detection Based on a Deep Neural Network Combined CNN and RNN

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) ◽

10.1109/ictai.2016.0081 ◽

2016 ◽

Cited By ~ 24

Author(s):

Haohao Jiang ◽

Yao Lu ◽

Jing Xue

Keyword(s):

Neural Network ◽

Event Detection ◽

Deep Neural Network ◽

Video Event ◽

Soccer Video ◽

Video Event Detection

Download Full-text

SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

INFORMS Journal on Computing ◽

10.1287/ijoc.2021.1112 ◽

2021 ◽

Author(s):

Yuheng Hu ◽

Yili Hong

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Event Detection ◽

Large Scale ◽

Short Term Memory ◽

State Of The Art ◽

Neural Network Models ◽

Neural Event ◽

End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

Download Full-text

A Non - Intrusive Load Identification Algorithm Combined with Event Detection

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666210617125742 ◽

2021 ◽

Vol 14 ◽

Author(s):

Runhai Jiao ◽

Qihang Zhou ◽

Liangqiu Lyu ◽

Guangwei Yan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Event Detection ◽

The State ◽

Event Recognition ◽

Identification Algorithm ◽

Load Identification ◽

Fusion Model ◽

Public Data ◽

Artificial Neural

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Result: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

Download Full-text