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Activity Recognition

Video analysis activities have been shifting from deducing the present situation to forecasting the future. Vision-based Activity Recognition is used to infer human actions in the present state, based upon complete action executions, and to predict human actions in the future state based upon incomplete action executions. These two variations have become particularly widespread lately because of their emerging real-world applications, such as mopping detection, wiping detection, speaking on the phone, aggressive behavior detection, etc.

Object Detection

The goal of object detection is to notice or discover the presence of real-world objects within an image or video frame and to be able to tell an object apart from the static background. Object detection algorithms typically use extracted features and learning algorithms to recognize instances of an object category. It is commonly used in applications such as image retrieval, security, surveillance, and automated vehicle parking systems. Some use cases of object detection include face recognition, object tracking, and anomaly detection.

Facial Recognition

Facial recognition is a category of biometric software that maps an individual’s facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint to verify an individual’s identity. With advancements in technology, it is now a viable option for authentication as well as identification. Facial recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. Its use cases include face ID, photograph tagging, smart advertisements by identifying face ID, authentication, and security.

Posture Detection

The goal of object detection is to notice or discover the presence of real-world objects within an image or video frame and to be able to tell an object apart from the static background. Object detection algorithms typically use extracted features and learning algorithms to recognize instances of an object category. It is commonly used in applications such as image retrieval, security, surveillance, and automated vehicle parking systems. Some use cases of object detection include face recognition, object tracking, and anomaly detection.

Pattern Detection

Pattern recognition is a programmed or learned ability to recognize patterns within data sets. It is a fast-developing field, which underpins developments in areas such as computer vision, image processing, text and document analysis, and neural networks. Machines are trained to recognize the required images based on a particular pattern, like identifying a person’s face based on a specific pattern. Video streams provide us access to vibrant temporal patterns that are captured using an AI engine. These temporal patterns present us with various spatial patterns that are used in applications like activity recognition, event detection/classification, anomaly detection, activity-based person identification, etc.

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