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Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression , augmented reality , traffic control, medical imaging [ 1 ] and video editing .
In 2003, Kiyoharu Aizawa introduced a context-based video retrieval system that was designed to handle data continuously captured from various sources, including a wearable camera, a microphone, and multiple sensors such as a GPS receiver, an acceleration sensor, a gyro sensor, and a brain-wave analyzer.
Using behavioral analytics, a self-learning, non-rule-based A.I. takes the data from video cameras and continuously classifies objects and events that it sees. For example, a person crossing a street is one classification. A group of people is another classification.
At the popular grocery store in North Carolina, artificial intelligence tracks customer behavior at self-checkout kiosks. To combat rising retail theft, Harris Teeter uses AI cameras to track self ...
IP cameras with intelligent video surveillance technology can be used to detect flame and smoke in 15–20 seconds or even less because of the built-in DSP chip. The chip processes algorithms that analyzes the videos captured for flame and smoke characteristics such as color chrominance, flickering ratio, shape, pattern and moving direction.
Another form of tracking permitted by ultrasonic tracking is cross-device tracking, which enables a user's profile to be connected across multiple devices based on proximity. [3] This form of tracking, in linking different devices, can help advertisers show more targeted ads or open individuals to attacks by malicious actors. [3]
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