Unusual Crowd Event Detection: An Approach Using Probabilistic Neural Network

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.

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Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

Scientific Research Journal ◽

10.24191/srj.v2i2.9330 ◽

2005 ◽

Vol 2 (2) ◽

pp. 25

Author(s):

Noraliza Hamzah ◽

Wan Nor Ainin Wan Abdullah ◽

Pauziah Mohd Arsad

Keyword(s):

Neural Network ◽

Power Quality ◽

Probabilistic Neural Network ◽

Microsoft Excel ◽

Waveform Data ◽

The Neural Network ◽

Harmonic Voltage ◽

Power Quality Disturbances ◽

Electronic Load ◽

Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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