iHuman-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. The goal of human-object interaction detection is to have machines identifying interactions that humans perform on objects. We provide a basic survey of the developments in the field of human object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. To provide insight to future researchers, we perform a study examining the performance of each component of a multi-stream architecture for human-object interaction detection. We examine the HORCNN architecture as a foundational work in the field. We also provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human object interaction detection. Lastly, we begin the construction of a human-object interaction benchmarking platform.