  Recent success in deep reinforcement learning is having an agent learn how to
play Go and beat the world champion without any prior knowledge of the game. In
that task, the agent has to make a decision on what action to take based on the
positions of the pieces. Person Search is recently explored using natural
language based text description of images for video surveillance applications
(S.Li et.al). We see (Fu.et al) provides an end to end approach for
object-based retrieval using deep reinforcement learning without constraints
placed on which objects are being detected. However, we believe for real-world
applications such as person search defining specific constraints which identify
a person as opposed to starting with a general object detection will have
benefits in terms of performance and computational resources required. In our
task, Deep reinforcement learning would localize the person in an image by
reshaping the sizes of the bounding boxes. Deep Reinforcement learning with
appropriate constraints would look only for the relevant person in the image as
opposed to an unconstrained approach where each individual objects in the image
are ranked. For person search, the agent is trying to form a tight bounding box
around the person in the image who matches the description. The bounding box is
initialized to the full image and at each time step, the agent makes a decision
on how to change the current bounding box so that it has a tighter bound around
the person based on the description of the person and the pixel values of the
current bounding box. After the agent takes an action, it will be given a
reward based on the Intersection over Union (IoU) of the current bounding box
and the ground truth box. Once the agent believes that the bounding box is
covering the person, it will indicate that the person is found.

Computer Vision and Pattern Recognition;Multimedia