Abstract: A tracking system and method are suited to tracking multiple of objects of different categories in a video sequence. A sequence of video frames is received and a set of windows is extracted from each frame in turn, based on a computed probability that the respective window contains an object, without reference to any specific category of object. For each of these windows, a feature representation is extracted. A trained detector for a selected category detects windows that constitute targets in that category, based on the respective feature representations. More than one detector can be used when there is more than one category of objects to be tracked. A target-specific appearance model is generated for each of the targets (e.g., learned or updated, if the target is present in a prior frame). The detected targets are tracked over one or more subsequent frames based on the target-specific appearance models of the targets.

Abstract: An object detection method includes for each of a set of patches of an image, encoding features of the patch with a non-linear mapping function, and computing per-patch statistics based on the encoded features for approximating a window-level non-linear operation by a patch-level operation. Then, windows are extracted from the image, each window comprising a sub-set of the set of patches. Each of the windows is scored based on the computed patch statistics of the respective sub-set of patches. Objects, if any, can then be detected in the image, based on the window scores. The method and system allow the non-linear operations to be performed only at the patch level, reducing the computation time of the method, since there are generally many more windows than patches, while not impacting performance unduly, as compared to a system which performs non-linear operations at the window level.

Abstract: A system and method enable generating a specific object detector for a category of interest. The method includes identifying seed objects in frames of a video sequence with a pre-trained generic detector for the category. An appearance model is iteratively learned for each of the seed objects using other frames in which the seed object is identified. The appearance models are learned jointly to optimize a loss function which accounts for the loss of incorrectly labeling sub-images and a regularization term which measures a distance between the appearance models. The loss of incorrectly labeling sub-images is determined using a motion model which predicts the location of the seed object in the subsequent frames so that sub-images outside the location that the current appearance model contribute to the loss. The specific object detector is then generated by aggregating the optimized appearance models.

Abstract: A system and method enable generating a specific object detector for a category of interest. The method includes identifying seed objects in frames of a video sequence with a pre-trained generic detector for the category. An appearance model is iteratively learned for each of the seed objects using other frames in which the seed object is identified. The appearance models are learned jointly to optimize a loss function which accounts for the loss of incorrectly labeling sub-images and a regularization term which measures a distance between the appearance models. The loss of incorrectly labeling sub-images is determined using a motion model which predicts the location of the seed object in the subsequent frames so that sub-images outside the location that the current appearance model contribute to the loss. The specific object detector is then generated by aggregating the optimized appearance models.