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We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals. At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-level image features, lead to a much richer representation of the physical interactions between targets compared to hand-specified social force models that previous works have introduced for tracking. One disadvantage of using social forces is that all pedestrians must be detected in order for the forces to be applied, while our method is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions. The interaction feature strings are used in a Random Forest framework to track targets according to the features surrounding them. Results on six publicly available sequences show that our method outperforms state-of-theart approaches in multiple people tracking.

Gesture recognition remains a very challenging task in the field of computer vision and human computer interaction (HCI).A decade ago the task seemed to be almost unsolvable with the data provided by a single RGB camera. Due to recent advances in sensing technologies, such as time-of-flight and structured light cameras, there are new data sources available, which make hand gesture recognition more feasible. In this work, we propose a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices.The depth images are used to derive rotation-, translation-and scale-invariant features. A multi-layered random forest (MLRF) is then trained to classify the feature vectors, which yields to the recognition of the hand signs.The training time and memory required by MLRF are much smaller, compared to a simple random forest with equivalent precision. This allows to repeat the training procedure of MLRF without significant effort.To show the advantages of our technique, we evaluate our algorithm on synthetic data, on publicly available dataset, containing 24 signs from American Sign Language(ASL) and on a new dataset, collected using recently appeared Intel Creative Gesture Camera.

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