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Computer Vision

Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

Computer vision is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly.

Computer Vision and Deep Learning

Computer vision’s goal is not only to see, but also process and provide useful results based on the observation. For example, a computer could create a 3-D image from a 2-D image, such as those in cars, and provide important data to the car and/or driver. For example, cars could be fitted with computer vision which would be able to identify and distinguish objects on and around the road such as traffic lights, pedestrians, traffic signs and so on, and act accordingly. The intelligent device could provide inputs to the driver or even make the car stop if there is a sudden obstacle on the road.

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

Deep Learning simplifies the process of feature extraction through the process of convolution. Convolution is a mathematical operation, which maps out an energy function, which is a measure of similarity between two signals, or in our case images.

Internet of Things

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines and the ability to transfer data over a network without requiring any human intervention in the process.

IoT encourages companies to rethink the ways they approach their businesses, industries and markets and gives them the tools to improve their business strategies.

An IoT ecosystem consists of web-enabled smart devices that use embedded processors, sensors and communication hardware to collect, send and act on data they acquire from their environments. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is either sent to the cloud to be analysed or analysed locally. Sometimes, these devices communicate with other related devices and act on the information they get from one another. The devices do most of the work without human intervention, although people can interact with the devices — for instance, to set them up, give them instructions or access the data.

Know about Deep Learning

The sheer complexity of video data makes it impossible for hand coded traditional techniques to perform in comparison to Deep Learning methods. Intelligent Video Analytics is required to filter and process data, trace moving objects, detect abnormalities, and generate alarms so that City authorities can take appropriate action. Mimicking the process of the human brain, the technique uses sophisticated, multi-level, deep neural networks (around 152 layers) to create systems that can perform feature detection from massive amounts of unlabeled training data.

What type of Analytics Capability does the system have?

Besides catering to the Smart City Urban Solution asks here is a list of other analytics capabilities :Crowd Motion Analysis, Queue Counting, Crowd Counting, People Attributes, Age, Gender, Action Detection (Fight, Littering etc), Emotion Detection, Face Recognition, License Plate Recognition, Abandoned Object, Area Count, Line Count, Loitering, Dwell Time, Person of Interest, Vehicle of Interest, Intrusion, Tailgating, Illegal Parking, Stationary Vehicles, Slow moving/ static traffic, Illegal Turns, Reverse Driving, Helmet Detection, Vehicle Counting and Classification, Traffic Incident Detection, Foreign objects and debris on the road, Junction delay, traffic jam and queuing time, Twin Detection, Detection by Color etc.

Our solution is developed with the state-of-the-art deep learning method that is able to deal with all the challenges mentioned above constantly in different types of scenes in all weather conditions indoor and outdoor. The deep learning model is specifically designed for the complicated environment compared to traditional video analytics technology. The Solution uses part-based representation and different layers in the neural network focus on different objects/events detection.