![]() Most of the applications demand this problem to be solved in real time. Some object instances can also be occluded. Small objects, particularly, give low performance at being detected because less information is available to detect them. There may not be enough data to accurately cover all the variations well enough. Apart from the variation in size the variation can be in lighting, rotation, appearance, background, etc. The instance can occupy very few pixels, 0.01% to 0.25%, as well as the majority of the pixels, 80% to 90%, in an image. The object categories in training and testing set are then supposed to be statistically similar. The images can have object instances from same classes, different classes or no instances at all. It is (usually) a supervised learning problem in which, given a set of training images, one has to design an algorithm which can accurately locate and correctly classify as many object instances as possible in a rectangle box while avoiding false detections of background or multiple detections of the same instance. The added challenge is to correctly detect the presence and accurately locate the object instance(s) in the image. Object detection is a natural extension of the classification problem. 5.1 Classical Datasets with Common Objects.4.3.2 Universal Detector, Lifelong Learning.4.2 Detecting Objects Under Constraints.4.1.2 Object Detection in 3D Point Clouds.4.1 Detecting Objects in Other Modalities.3.1 Complementary New Ideas in Object Detection.2 On the Design of Modern Deep Detectors.1.1 From Hand-crafted to Data Driven Detectors. ![]()
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