![]() Īction recognition methods can be based on a number of different sources of features such as space-time interest points, improved trajectories of features and fisher vectors. They include automated security monitoring, social applications intelligent transportation smart hospitals and homes. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Īction recognition is a key step in many amazing applications areas. Three public-domain data-sets, namely MSR 3D Action, Northwestern UCLA multi-view actions and MSR 3D daily activity, are used to evaluate the proposed solution. The developed approach is capable of recognising both human action and human–object interaction. Average score fusion is used on the output. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multi-class support vector machines. These help to identify and differentiate between small object interactions. ![]() Dedicated 3D CNN streams for multi-time resolution appearance information are also included. The region-adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. ![]() Multiple views are synthesised to enhance the view invariance. Multi-stream 3D convolutional neural networks (CNNs) are trained on the different views and time resolutions of the region-adaptive depth motion maps. It uses a novel multi-view region-adaptive multi-resolution-in-time depth motion map (MV-RAMDMM) formulation combined with appearance information. This work proposes a novel action recognition system. ![]() Human action recognition remains an important yet challenging task. ![]()
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