Object Detection, Classification & Tracking
Detect and Classify Objects from Images and Videos in Real-time
KritiKal’s object detection and classification solutions are designed from scratch to enable clients in precisely detecting & classifying objects with a higher level of accuracy. We have extensive experience of developing various object detection, recognition/classification and tracking algorithms. Our expertise derives from having worked on projects related to road sign detection and analysis, automatic vehicle number plate reading systems, aircraft detection in aerial/satellite images, face recognition, vehicle make/model detection, surgical instruments detection etc.
Object Detection & Localization
It deals with detecting instances of objects such as pedestrians or vehicles in images or videos. Object Detection and Localization are done to detect and locate the object. It aims to locate most visible object in an image such as pedestrians or vehicles in images or videos. At KritiKal, we build convolution neural networks for object localization using Deep Learning along with Machine Learning and core image processing techniques, and then apply it to image data to execute a wide range of applications such as face recognition, vehicle tracking, self-driving etc.
It can be defined as the process of locking on to a moving object and then identifying whether the object is same as in the previous frame and in which direction it is moving. It includes various algorithms such as Kalman filter. KritiKal works on object tracking algorithms for diverse application from vehicle classification to remote surveillance to locate a moving object using the surveillance data in real-time.
Density analysis helps in crowd motion patterns and vehicle density analysis. It can be applicable to various real-time use cases such as high gathering events, automatic traffic control signals, highly secured premises, etc. We employ clustering & tracking algorithms with Deep Learning and data analytics to calculate the density regions in a scene.
Object classification involves training of a model on a dataset of specific objects. This model then classifies new objects as belonging to one or more of the training categories. At KritiKal, we work on a wide range of classification problems which tends to classify a wide range of objects for different usecases using different machine learning, deep learning techniques
Unknown Object Detection
Many object detection systems have the prior knowledge about the objects or human interference. However, an automated system running in a real-world scenario may often confront with previously unseen objects. At KritiKal we use object segmentation methods and machine learning techniques to identify unknown objects from the scene.
Handling Data Variations (Illumination, Rotation, etc.) in Detection & Classification
In real-time environment, we cannot control external factors like sunlight, shadows, which may lead to over brightness & blurriness of images. At KritiKal, we develop models that are robust enough to detect and handle such variation cases depending on usecase.
Community Tools Supported
The Computer Vision & Image Processing practice at KritiKal comprises of experts who have worked on various open source and proprietary business intelligence tools, some of which are outlined below: