AI Solutions
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We Have Done
Gait Recognition
Algorithm: Detects contours, Siames model, Silhouettes, Feature extraction algorithm
System Requirements: GPU
The GAIT recognition system uses the body's shape and movement to recognize the gait. This software uses CV algorithms to detect a human silhouette and analyze its movements. These data are used to create a human behaviour model. Computer Vision (CV), a technique that identifies humans by moving in video, has made it possible to use natural biometric features (e.g., the human skeleton, silhouette and change while walking) as well as abstract features.
Biometric Solutions
Algorithm: Fingerprint recognition using traditional methods like ridge extraction, minutiae extraction, sweat pores-based features ( Level 1, Level 2, Level 3 features) and image processing-based descriptors. Deep learning-based models like the Siamese model to obtain the feature. Face recognition using Deep learning-based models and traditional methods like Dlib. Mediapipe.
System Requirements: GPU or CPU
Biometric authentication technology has been an important industry trend for years, especially in recent years due to the latest AI Innovations available on the market. Different Biometric solutions like Face recognition, Fingerprint identification, Iris recognition, Gait recognition can be deployed on the CPU and GPU. Vision-based biometric solutions can be deployed using web and IP (CCTV) cameras
3D Point Cloud processing
Algorithm: Deep learning based solution.
System Requirements: GPU
A point cloud is a 3D representation suitable for processing real-world data, especially when the geometry of the scene/objects is required, such as the distance, the shape and the size of objects. Different solutions for 3D point clouds like point cloud registration. Image-to-point cloud conversion. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.
Fight & Fall Detection and Human Activity recognition
Algorithm: Sequential models and CNNs are used to extract features like motion between people, and classification is done for these features to obtain the ground truth motion.
System Requirements: GPU
The objective of this system is to detect anomalies like a fight or fall in video sequences. When these anomalies are detected alerts are stored with their corresponding flags and images in the database. It can also be used with trackers to detect a particular person's fall. Models can be customized for detecting a particular type of motion only. We can also detect different motions like walking, sitting, and lying down.
Facial Landmark Detection & human Pose estimation
Algorithm: Custom Deep learning based models.
System Requirements: GPU
Facial landmark detection is the task of detecting key landmarks on the face and tracking them (being robust to rigid and non-rigid facial deformations due to head movements and facial expressions). Custom facial landmark detection allows users to decide which landmarks they especially want to detect and at which angle they want to use deep learning. Human pose estimation detects different landmarks on the body of a person.
Traffic and Parking Management System
Algorithm: OCR, Object detection, Trackers.
System Requirements: GPU
AI-based smart parking solutions have become a crucial part of our daily lives. They are important and beneficial for both parking managers and users. The traffic management system includes automatic number plate recognition, Vehicle speed detection, Vehicle direction detection, and crowd analysis. In this system, we have used two models: one for detecting number plates using YOLO, and the second for detecting text using OCR on number plates.
Fire Detection
Algorithm: Convolutional Neural Networks (CNNs), Long short-term memory (LSTMs), Object detection
System Requirements: GPU
The fire detection system's objective is to detect and identify fire. The flag is raised once the fire has been detected. Modern advances in embedded Processing allow vision-based systems to detect fire using Convolutional Neural Networks during surveillance. We have two models to predict fire in videos, one based on sequential models and one based on object detection. The model was tested on IP cameras (CCTV Cameras), which provided high accuracy and real-time detection.
Football Game Analysis
Algorithm: This project uses methods like Object detection, Keypoint detection, Homography, and Perspective transformation.
System Requirements: GPU
Visual sports analytics have recently gained popularity, particularly in player and ball detection, action recognition, and camera pose estimation in various sports. The sport in which people are most interested is football or soccer. The creation and execution of a low-cost methodology to extract football analytics from a video of a football game using solely machine learning techniques is the goal of this system.
Sentiment Analysis for Text
Algorithm: Basic Natural Processing based algorithms
System Requirements: CPU and GPU
A sentiment analysis system is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs. Some popular sentiment analysis applications include social media monitoring, customer support management, and analyzing customer feedback.
Crowd analysis
Algorithm: Object detection , trackers
System Requirements: CPU and GPU
The Crowd analysis system uses deep learning methods for any particular area or premises. Different insights are extracted and stored in a database to analyze the pattern and behaviour of crowds in particular premises like shopping malls, pharma companies, public areas, Shop counters, events, etc. Using tracker and object detection model to analyze the total number of visits to a particular area or premises.
Object Tracking System
Algorithm: Centroid based tracking, Deep sort
System Requirements: CPU and GPU
Object tracking is a deep learning process where the algorithm tracks the movement of an object. To put it another way, the challenge is estimating or forecasting the locations and other pertinent details of moving objects in a video. Object detection is typically a step in the object-tracking process. Numerous application cases utilizing various types of input footage use object tracking. The techniques used to build object-tracking apps are affected by whether or not the anticipated input will be an image, a video, or a real-time video as opposed to a prerecorded video.
GANs Solution
Algorithm: Deep learning based algorithms for GANs.
System Requirements: GPU
Generative Adversarial Networks are deep learning machines that combine two separate models into one architecture. Based on a training data set, a GAN learns to generate new data with the same statistics as the training set. The data created by the GAN can be anything, such as images, videos, or text. Different Solutions related to GANs like image denoising, image colouring, fake image generation, and image enhancement.
Advanced Machine Learning for Tabular & Time-Series Data
Algorithm: Support vector machine, Xgboost, Ensemble methods, Stacking regressors, Stacking classifiers.
System Requirements: CPU and GPU
Time series forecasting can be used by any business or organization dealing with continuously generated data and the requirement to adjust to operational shifts and changes. Time series solutions like predicting sales and demand forecasting, web traffic forecasting, and stock price forecasting using machine learning and deep learning. Advanced machine learning solutions like Support vector machine, Xgboost, and Stacking classifiers to work on tabular data.