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In this work we present a deep convolutional neural network using 3D convolutions for Gait Recognition in multiple views capturing spatio-temporal features. A special input format, consisting of the gray-scale image and optical flow enhance color invaranice. The approach is evaluated on three different datasets, including variances in clothing, walking speeds and the view angle. In contrast to most state-of-the-art Gait Recognition systems the used neural network is able to generalize gait features across multiple large view angle changes. The results show a comparable to better performance in comparison with previous approaches, especially for large view differences.

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In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle various video scenes. Additionally, we propose a new approach to estimate background model from video. For the training of the CNN, we employed randomly 5% video frames and their ground truth segmentations taken from the Change Detection challenge 2014(CDnet 2014). We also utilized spatial-median filtering as the postprocessing of the network outputs. Our method is evaluated with different data-sets, and the network outperforms the existing algorithms with respect to the average ranking over different evaluation metrics. Furthermore, due to the network architecture, our CNN is capable of real time processing.

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