Human activity detection outdoors is emerging as a very important research field due to its potential application in surveillance, assisted living, search and rescue, and military applications. For such applications it is important to have detailed information about the human target, for example, whether the detected target is a single person or a group of people, what activity a target is performing, and the rough location of the target. In this paper, we propose novel usage of machine learning techniques to perform subject classification, human activity classification, people counting, and coarse localization by classifying micro-Doppler signatures obtained from a low-cost and low-power radar system. Our experiments were performed outdoors. For feature extraction of micro-Doppler signatures, we applied a two-directional two-dimensional principle component analysis (2D2PCA). Our results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and knearest neighbors (kNN) classifiers were greatly improved. We also designed and implemented a Convolutional Neural Network (CNN) for the target classifications in terms of type, number, activity and coarse localization. Our CNN model obtained very high classification accuracies (97% to 100%), which are superior to the best results obtained by SVM and kNN. Finally, we investigated the effects of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used on the classification performance, providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications.

Motion trajectories contain rich information about human activities. We propose to use a 2D LIDAR to perform multiple people activity recognition simultaneously by classifying their trajectories. We clustered raw LIDAR data and classified the clusters into human and non-human classes in order to recognize humans in a scenario. For the clusters of humans, we implemented the Kalman Filter to track their trajectories which are further segmented and labelled with corresponding activities. We introduced spatial transformation and Gaussian noise for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition (HAR). Finally, we built two neural networks including a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to classify trajectory samples into 15 activity classes collected from a kitchen. The proposed TCN achieved the best result of 99.49% in overall accuracy. In comparison, the TCN is slightly superior to the LSTM network. Both the TCN and the LSTM network outperform hidden Markov Model (HMM), dynamic time warping (DTW), and support vector machine (SVM) with a wide margin. Our approach achieves a higher activity recognition accuracy than the related work.

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