Activity recognition relates to the automatic visual detection and interpretation of human behaviour and is emerging as an active domain of computer vision. It has important applications such as identifying individuals who are at risk of suicide in public locations such as bridges or railway stations. These individuals are known to exhibit easily observable activities and behaviours such as pacing, looking up and down the railway tracks, and leaving objects on the platform. In order to detect these behaviours, an approach to individual person activity recognition is needed which can run in real time and monitor multiple individuals in parallel. We present a method for human activity recognition using skeletal keypoints and investigate how using varying sample rates and sequence lengths impacts accuracy. The results show that for any given sequence length, optimising the sample rate can result in an overall increase in classification accuracy and improvement in run-time. Results demonstrate that finding the optimal time period over which to sample frames is more important than simply decreasing the number of frames sampled. Further, we show that keypoint based activity recognition approaches outperform other state of the art approaches. Finally, we show that this approach is fast enough for real time activity recognition when up to 14 people are present in the image whilst maintaining a high degree of accuracy.
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