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We propose a deep-learning approach for people detection on depth imagery. The approach is designed to be deployed as an autonomous appliance for identifying people attacks and intrusion in video surveillance scenarios. To this end, we propose a fully-convolutional and sequential network, named WatchNet, that localizes people in depth images by predicting human body landmarks such as head and shoulders. We use a large synthetic dataset to train the network with abundant data and generate automatic annotations. Adaptation to real data is performed via fine tuning with real depth images. The proposed method is validated in a novel and challenging database with about 29k top view images collected from several sequences including different people assaults. A comparative evaluation is given between our approach and other standard methods, showing remarkable detection results and efficiency. The network runs in 10 and 28 FPS using CPU and GPU, respectively.
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Pascal Fua, Vincent Lepetit, Yuki Ono, Mathieu Salzmann, Eduard Trulls Fortuny, Kwang Moo Yi
black-boxes''. The Law of Parsimony states that simpler solutions are more likely to be correct than complex ones''. Since they perform quite well in practice, a natural question to ask, then, is in what way are neural networks simple?
We propose that compression is the answer. Since good generalization requires invariance to irrelevant variations in the input, it is necessary for a network to discard this irrelevant information. As a result, semantically similar samples are mapped to similar representations in neural network deep feature space, where they form simple, low-dimensional structures.
Conversely, a network that overfits relies on memorizing individual samples. Such a network cannot discard information as easily.
In this thesis we characterize the difference between such networks using the non-negative rank of activation matrices. Relying on the non-negativity of rectified-linear units, the non-negative rank is the smallest number that admits an exact non-negative matrix factorization.
We derive an upper bound on the amount of memorization in terms of the non-negative rank, and show it is a natural complexity measure for rectified-linear units.
With a focus on deep convolutional neural networks trained to perform object recognition, we show that the two non-negative factors derived from deep network layers decompose the information held therein in an interpretable way. The first of these factors provides heatmaps which highlight similarly encoded regions within an input image or image set. We find that these networks learn to detect semantic parts and form a hierarchy, such that parts are further broken down into sub-parts.
We quantitatively evaluate the semantic quality of these heatmaps by using them to perform semantic co-segmentation and co-localization. In spite of the convolutional network we use being trained solely with image-level labels, we achieve results comparable or better than domain-specific state-of-the-art methods for these tasks.
The second non-negative factor provides a bag-of-concepts representation for an image or image set. We use this representation to derive global image descriptors for images in a large collection. With these descriptors in hand, we perform two variations content-based image retrieval, i.e. reverse image search. Using information from one of the non-negative matrix factors we obtain descriptors which are suitable for finding semantically related images, i.e., belonging to the same semantic category as the query image. Combining information from both non-negative factors, however, yields descriptors that are suitable for finding other images of the specific instance depicted in the query image, where we again achieve state-of-the-art performance.Pascal Fua, Vincent Lepetit, Yuki Ono, Mathieu Salzmann, Eduard Trulls Fortuny, Kwang Moo Yi