Uncanny Vision Uses Deep Learning To Sense Unfamiliar Happenings

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In the history of life on earth, animals gained the gift of organic vision approximately 700 million years ago. Over time, we were able to use technology to invent the camera and machine vision, with functions similar to human eyes. To use these visuals and take decisions based on various situations in which things could get involved needs higher intelligence.

With the Internet of Things (IoT), it is now possible to bring in awareness to such digital eyes and keep ourselves informed about something out of the ordinary or unfamiliar happening around us. Uncanny Vision, a startup founded by Ranjith Parakkal and Navaneethan Sundaramoorthy, has a product revolving around this innovative idea of teaching the IoT device to identify objects using an analytical system powered by deep learning and smart algorithms. At the grassroots level, the system needs to be taught about various images and visuals that it needs to familiarise with for it to understand how these normally appear. In essence, it is like teaching a child about the world and correcting the perception in order to set proper benchmarks.

Learning the unfamiliar or uncanny

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In the biological world, vision is crucial for survival. Visual data helps us make better decisions on various activities. Take the example of a surveillance camera designed to watch over a specific place frequently visited by people on the street. The camera seems to have no other objective apart from staring at a fixed point-of-interest. In the case of a mishap, what if this camera could trigger an important push notification to the nearest police station and help the police arrive in a jiffy? Now that could only be accomplished if an extra awareness feature is added to the set-up so that it can carry out better surveillance while simplifying it for the human involved in decision making.

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Fig. 1: Edge based vision

Uncanny Vision works on two prime areas. One is on the edge, which is at the user’s end and the other on the IoT-Cloud infrastructure. This combination is very powerful when it comes to making human-like decisions. The whole system is like an intelligent being that can recognise anything it is programmed to see—even a moving object.

When we say programmed to see, there are a couple of ways that this can be achieved. One way is to train a model (neural network) with thousands of labelled images of target objects that you would like the camera to recognise, for example, gun, helmet, people or wallet. This trained model is optimised and programmed onto the camera hardware. The camera can then recognise objects with reasonable accuracy.

Another way is to have a self-learning camera. In this scenario, the camera (pre-loaded with an untrained model) is given a certain amount of video data and is told that everything in that sample data is normal behaviour. The model learns the definition of normal with the given sample data. The camera is then switched to surveillance mode. It watches and flags any behaviour that it has not seen before, as an anomaly.

An excellent application of this would be to track human movement in an automated teller machine (ATM). These places are often visited by people to swipe cards and get cash out. It might not look like a fast-moving assembly line, but on any given day thousands of people visit ATMs. Let us take an example. If you approach an ATM and do not stand in the position that the system normally recognises, it flags it as an anomaly or unfamiliar sighting.

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Smart surveillance is achieved here by giving intelligent sight to the camera itself. If you normally withdraw money and leave, the system does not trigger an anomaly, but if you try to reach out to the ATM in an unfamiliar way, the analytical system evaluates the events based on a scale, which spans from red to green.

Any normal human activity that is expected, for example, standing or walking, would be in the green zone, whereas an unfamiliar or unexpected activity such as you falling down, crouching down or with arms raised up above the head would be in the red zone.

When the system realises that an activity has triggered the red zone, an alert is sent to the responsible personnel or emergency system that it has been programmed to contact. The system can also be taught to detect unauthorised objects that people wear such as helmets or headscarves for early detection of normal behaviour.

Algorithms that learn and adapt

Deep learning may sound fancy but it forms the heart of the system. Uncanny Vision’s computer vision has 70-plus algorithms that cover everything from pedestrian detection, vehicle detection, histograms or holography estimation, and is faster than OpenCV (an open source software used for image processing).

Fig. 2: Human activity monitoring in green
Fig. 3: Human activity monitoring in red

Computer vision in Uncanny Vision is targeted to and optimised for an ARM processor. A new set of cameras that Uncanny works on is similar to the ones used in smartphones as these are equipped with graphic processors and other processing cores for fulfilling all hardware requirements for evaluating the visual sequences captured by these.

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Evaluating visual sequences

Processing of data starts after the images reach the CMOS sensor on the camera. From here, these are taken to an image signal processor that cleans up the image from the sensor and then hands it to the main application running on the CPU. All of this happens inside a system-on-chip like Qualcomm’s Snapdragon chips used in most smartphones these days.

This is where Uncanny Vision’s software takes over and interprets the image using deep learning algorithms and convolutional neural network models. These convolutional neural network software models are able to recognise various useful information like human actions and faces from video input, and provide the analytics needed for a surveillance system.

The convolutional neural network models are first trained on GPU based Cloud computers using a tool called Caffe with labelled sample data. Uncanny Vision’s UncannyDL software uses these models in the camera where the hardware equipped with CPU, GPU and brewed models inside the endpoint device detects any anomalies, and pushes out a photo, video, metadata or a decision to the central Cloud platform.

Caffe is a deep learning framework made with expression, speed and modularity. Uncanny Vision uses Caffe for training convolutional neural network models with maximum accuracy and specificity of whatever it sees and identifies. Once the images from video footages are in, deep learning Caffe models kick in and refer to 1000 or more objects it has been taught to recognise. The system can also be taught to recognise animals, human poses and defects.

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