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We describe a study that aims to understand physical activity and sedentary behavior in free-living settings. We employed a wearable camera to record 3 to 5 days of imaging data with 40 participants, resulting in over 360,000 images. These images were then fully annotated by experienced staff with a rigorous coding protocol. We designed a deep learning based classifier in which we adapted a model that was originally trained for ImageNet [1]. We then added a spatio-temporal pyramid to our deep learning based classifier. Our results show our proposed method performs better than the state-of-the-art visual classification methods on our dataset. For most of the labels our system achieves more than 90% average accuracy across different individuals for frequent labels and more than 80% average accuracy for rare labels.

This work describes and explores novel steps towards activity recognition from an egocentric point of view. Activity recognition is a broadly studied topic in computer vision, but the unique characteristics of wearable vision systems present new challenges and opportunities. We evaluate a challenging new publicly available dataset that includes trajectories of different users across two indoor environments performing a set of more than 20 different activities. The visual features studied include compact and global image descriptors, including GIST and a novel skin segmentation based histogram signature, and state-of-the art image representations for recognition, including Bag of SIFT words and Convolutional Neural Network (CNN) based features. Our experiments show that simple and compact features provide reasonable accuracy to obtain basic activity information (in our case, manipulation vs. nonmanipulation). However, for finer grained categories CNNbased features provide the most promising results. Future steps include integration of depth information with these features and temporal consistency into the pipeline.

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