Real-time crime detection using convolutional LSTM and YOLOv7

Real-time crime detection using convolutional LSTM and YOLOv7

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiske detaljer
Main Authors:	Sakiba, Cyrus, Tarannum, Syeda Maisha, Nur, Farzana, Arpan, Fahad Faisal, Anzum, Ahnaf Ahmed
Andre forfattere:	Rahman, Md. Khalilur
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Deep learning Bidirectional LSTM YOLOv7 YOLOv4 MobilenetV2 Violence prediction Realtime Machine learning Cognitive learning theory Real-time data processing
Online adgang:	http://hdl.handle.net/10361/22038

Lignende værker

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Deep learning-based real-time pothole detection for avoiding road accident
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Multimodal approach to human detection in unconstrained environments using YOLOV7 for conventional, infrared & thermal cameras
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An integrated approach: fake review detection using convBERT-BiLSTM classification
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Intracranial hemorrhage detection using CNN-LSTM fusion model
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Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning
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Bangla speech recognition using 1D CNN and LSTM with different dimension reduction techniques
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A computer vision based approach for stalking detection using CNN-LSTM hybrid model
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