Object Detection with Vocabularies of Space-time Descriptors

This paper presents a novel framework for objects detection in security and broadcast videos. Our method assumes that object classes are unknown in advance and exploit the temporal-space properties of the videos for the creation of a vocabulary that describes these classes. Local space-time features have recently became a popular video representation for action recognition and object detection. Several methods for feature localization and description have been proposed in the literature and promising recognition results were demonstrated for a number of action classes. In this work we propose the use of different kinds of descriptors for the creation of vocabularies for different detection object task. For a better description of the videos we carry out a background model, tryring to clean up and follow the areas where there are objects. The points of interest in the videos to characterize the objects are calculated with a temporary variant of the famous Harris corner detector. With the descriptors obtained from the points of interest, a vocabulary is realized usingthe kinds of videos we want to train. Then we obtained the frequency histograms between the videos for training and the vocabulary so, with a binary classifier obtain the trained classes and following the same procedure without the vocabulary realized the detection and monitoring of the objects. The new method presented is also compared with a state of the art method, obtaining better results in both accuracy and false object rejection.

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