  Activity recognition is the ability to identify and recognize the action or
goals of the agent. The agent can be any object or entity that performs action
that has end goals. The agents can be a single agent performing the action or
group of agents performing the actions or having some interaction. Human
activity recognition has gained popularity due to its demands in many practical
applications such as entertainment, healthcare, simulations and surveillance
systems. Vision based activity recognition is gaining advantage as it does not
require any human intervention or physical contact with humans. Moreover, there
are set of cameras that are networked with the intention to track and recognize
the activities of the agent. Traditional applications that were required to
track or recognize human activities made use of wearable devices. However, such
applications require physical contact of the person. To overcome such
challenges, vision based activity recognition system can be used, which uses a
camera to record the video and a processor that performs the task of
recognition. The work is implemented in two stages. In the first stage, an
approach for the Implementation of Activity recognition is proposed using
background subtraction of images, followed by 3D- Convolutional Neural
Networks. The impact of using Background subtraction prior to 3D-Convolutional
Neural Networks has been reported. In the second stage, the work is further
extended and implemented on Raspberry Pi, that can be used to record a stream
of video, followed by recognizing the activity that was involved in the video.
Thus, a proof-of-concept for activity recognition using small, IoT based
device, is provided, which can enhance the system and extend its applications
in various forms like, increase in portability, networking, and other
capabilities of the device.
