Direction and gender classification using convolutional neural network for side-view images captured from a monitored trail

Prior work by a subset of authors led to a development of optical camera platform, which has a capability of distinguishing between human and animal movement in an outdoor environment. Once the image is classified as a human, an idea of providing additional information and insights led to the exploration of gender (men vs women) and direction (left to right vs right to left) classifications. The proposed method classifies the human gender, based on the full body image oriented in a side view manner. An additional feature is to classify the direction of the movement. In the current paper, the Convolutional Neural Networks (CNNs) are used to distinguish between men and women gender classes and to identify the direction of the movement. Furthermore, different aspects of CNN are visualized (for example, attention heat maps, t-Distributed Stochastic Neighbor Embedding plot, etc.) to provide useful insights corresponding to classifications and misclassifications. Additionally, different CNN architectures were tried to figure out the best possible choice. The classification accuracies for gender (men vs women) and direction classification (left to right vs right to left) on the test data are close to 93.3% and 94%, respectively.