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In this project we propose a simple method for automatically determining the number of objects in an image . Once the number of objects are determined the objects per unit area or the density can also be estimated. Existing methods involve counting based on area of objects, color of objects, applying edge detection techniques etc.
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
All detection techniques are based on modelling the background of the image, i.e. set the background and detect which changes occur. Defining the background can be very difficult when it contains shapes, shadows, and moving objects. In defining the background, it is assumed that the stationary objects could vary in color and intensity over time.
Feature detection includes methods for computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Object Detection and Recognition: Identifying and recognising objects within images, especially in complex scenarios with multiple objects and occlusions, poses a significant challenge. Data Annotation and Labelling : Labelling diverse and multiple images for machine recognition is crucial for further processing accuracy, as incorrect ...
Recognition, the type object can be discerned, a person versus a car (4 +/− 0.8 line pairs) Identification, a specific object can be discerned, a woman versus a man, the specific car (6.4 +/− 1.5 line pairs) These amounts of resolution give a 50 percent probability of an observer discriminating an object to the specified level.
In their original human detection experiment, Dalal and Triggs compared their R-HOG and C-HOG descriptor blocks against generalized Haar wavelets, PCA-SIFT descriptors, and shape context descriptors. Generalized Haar wavelets are oriented Haar wavelets, and were used in 2001 by Mohan, Papageorgiou, and Poggio in their own object detection ...