A Fast Resource Efficient Method for Human Action Recognition

This paper describes a simple yet resource efficient method to train and finally test a classifier based on "Sussex-Huawei Locomotion- Transportation (SHL) dataset" for "Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge". Our team, "Maximum Analytics" placed special emphasis on simple feature design and fast training and recognition. The computational resources necessary for training a classifier based on the whole dataset was not present, therefore, an effective subset was chosen from the entire dataset. This approach also solved the class imbalance present in the initial dataset. Then we train the subset using popular machine learning algorithms. The maximum overall accuracy rate found is 82.8%.