Towards the Development of a Real-time Deep Surveillance Industrial Scene Human Activity Recognition System to Mitigate Gas Pipe-lines Vandalization


Authors : Onuh Gabriel, Akan J. Bello

Volume/Issue : Volume 4 - 2019, Issue 12 - December

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/2GeBYNq

Human activity detection and recognition (HADR) is an important field of study in computer vision, due to it wide application areas like security surveillance, robotics, human computer interaction, content based retrieval and annotation, human fall detection etc.,. The essence of HADR is to automatically understand and recognized what kind of action (human behavior and activities) is performed by human in a video captured by a surveillance system. This is really a difficult problem due to many challenges involved in HADR. These challenges include: cluttered backgrounds, variation in motion and human shape, variation in illumination conditions, occlusion, and viewpoint variations. However, the intensity of these challenges may vary depending on the category of an activity under consideration. Generally, the activities are grouped into four classes which constituent, human- gestures, actions, interactions, and group activities, this division is mainly based on complexity and duration of the activities. Due to the advancement in sensor and visual technology, HADR based systems have been widely used in many real-world applications. Specifically, the increase of small size sensors have enabled the smart devices to recognize the human activities in a context-aware manner. Hence, with HADR numerous application area we propose a deep surveillance industrial scene human activity detection to fight against gas pipe-line vandalizing, where, the recognition scheme can effectively detect any suspicious activity and report via sending a notification to the authorities for immediate action.

Keywords : Component; Real-Time Surveillance; Human Activity Recognition; KNN; Wireless Sensor Networks.

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