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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]
An illustration of their capabilities is given by the ImageNet Large Scale Visual Recognition Challenge; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition. [41] Performance of convolutional neural networks on the ImageNet tests is now close to that of humans. [41]
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.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Classification, object detection 2005 [33] MIT Computer Science and Artificial Intelligence Laboratory: PASCAL VOC Dataset Images in 20 categories and localization bounding boxes. Labeling, bounding box included 500,000 Images, text Classification, object detection 2010 [34] [35] M. Everingham et al. CIFAR-10 Dataset
BGSLibrary includes the original LBP implementation for motion detection [12] as well as a new LBP operator variant combined with Markov Random Fields [13] with improved recognition rates and robustness. dlib, an open source C++ library: implementation. scikit-image, an open source Python library. Provides a c-based python implementation for LBP
To search for the object in the entire frame, the search window can be moved across the image and check every location with the classifier. This process is most commonly used in image processing for object detection and tracking, primarily facial detection and recognition. The first cascading classifier was the face detector of Viola and Jones ...
SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale , University of California, Berkeley , and Stanford University . In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.