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As video content becomes increasingly mainstream, the capability to rapidly extract intelligence from live and recorded footage will be valuable. Applying machine learning algorithms to video feeds, we break down the raw footage to elicit information that can improve business outcomes in real time.
Apart from security, video analysis finds application in product marketing, patient care, transportation, and multiple other areas. Insights from video content analysis are being increasingly used to transform operations and drive efficiency in these sectors.
Thermal cameras connected to video analytics software can detect movement even in darkness and warn of trespass in real time. Security staff can visually verify the situation and take relevant action.
Object identification in live traffic streams reveal vehicle pile-ups and possible road congestion. License plate recognition can automate entry and exit at parking lots or enable free traffic flow on toll roads.
Staff need not carry ID cards. Cameras capture them entering the workspace and facial recognition systems identify individuals to automatically mark their attendance.
Video analytics serves to identify hotspots within retail outlets from CCTV recordings. Endcaps and shelf layouts can be arranged accordingly. Video-based people counting helps retailers adjust staff levels relative to footfall.
Analysis of live video streams from customer service desks or checkout counters helps detect crowd formation. This allows management to proactively step in to reduce wait times and crowding at customer service points.
In-store video analytics holds the potential to optimize store operations and improve product sales. From gender recognition to heat map and behavior analysis, video data can be subjected to machine learning algorithms to identify objects, detect movement, and recognize patterns enabling multiple insights for a retailer.
The most common application of video analytics lies in ensuring security. Feeds from surveillance cameras are analyzed in real time to detect untoward events and prevent security breaches in a multitude of settings.
Integration of video analysis with an IoT application enables more sophisticated decisions. When cameras become IoT sensors, a much wider range of inputs can be collected for analysis. For instance, replacing beacons with cameras to locate and track visitors in a retail store provides additional information such as demographic data. Within organizations, video-enabled IoT solutions can automate attendance tracking as well as monitor activities of employees and visitors.
In a smart manufacturing unit, quality-control monitoring with real-time video analytics processed on the edge helps to detect and avoid costly defects. The quick analysis of video data at the edge localizes decision making, reducing the latency significantly. Edge processing also enhances security and saves bandwidth by eliminating the transmission of data to the cloud. Additionally, identifying and tracking human behavior at workstations or production floors allows to identify non-productive work hours.
Open source software library with over 2500 computer vision and machine learning algorithms.
Software library developed by Google that can be used for machine learning applications.
API and SDK for detecting faces, emotions, and demographics such as age and gender.
Software system powered by Caffe2 deep learning framework that implements object detection algorithms.
Deep neural networks for facial detection, head-pose estimation, and eye-gaze estimation.
Widely used technique to detect objects where the foreground of an image, which contains objects of interest, is extracted for further processing.
A deep convolutional network trained to solve face verification, recognition, and clustering problems with high accuracy.
C++ toolkit containing machine learning algorithms and tools for detecting objects in images.
Facial recognition systems that can identify or verify a person from a digital image or video find application in a variety of contexts. Tag suggestions on Facebook, automated criminal identification from image/video footage, and access control integrated with facial biometrics are all facial recognition software in use.
Facial recognition works in two parts: face detection and face identification. In the first stage, the system detects faces in the input data using methods like background subtraction. Next, it measures the facial features to define facial landmarks and tries to match them with a known dataset. Based on the percentage of accuracy of match, the faces can be recognized or classified as unknown.
For instance, we used Dlib’s face landmark predictor to detect a face and extract features such as eyes, mouth, brows, nose, and jawline. The image was standardized by cropping to include just these features and aligning it based on the location of eyes and the bottom lip. The preprocessed image was then mapped to a numerical vector representation. An algorithmic comparison of the vector images made facial recognition possible.
The employee stands in front of the camera for a few seconds allowing it to capture his/her image. An integrated facial recognition system verifies the image with its training dataset and marks attendance on successful match.
The system detects the absence of a device from the shelf using background subtraction of CCTV images. With facial recognition capabilities, it will identify the person who entered the room during the time frame and assign the device to that employee.