| dc.description.abstract | Object detection and tracking are key features in many computer vision applications. Most state
of the art models for object detection, however, are computationally complex. The goal of this
project was to develop a fast and light-weight framework for object detection and object tracking
in a sequence of images using a Raspberry Pi 3 Model B, a low cost and low power computer.
As even the most light-weight state of the art object detection models, i.e. Tiny-YOLO and
SSD300 with MobileNet, were considered too computationally complex, a simplified approach
had to be taken. This approach assumed a stationary camera and access to a background image.
With these constraints, background subtraction was used to locate objects, while a light weight
object recognition model based on MobileNet was used to classify any objects that were found. A
tracker that primarily relied on object location and size was used to track distinct objects between
frames.
The suggested framework was able to achieve framerates as high as 7.9 FPS with 1 object in the
scene, and 2.9 FPS when 6 objects were present. These values are significantly higher, more than
7 times for 1 object and 2.6 times for 6 objects, than those achieved using the mentioned state of
the art models. This performance, however, comes at a price.
While the suggested framework was seen to work well in many situations, it does have several
weaknesses. Some of these include poor handling of occlusion, a lack of ability to distinguish
between objects in close proximity, and false detections when lighting conditions change.
Additionally, its processing speed is affected by the number of objects in an image to a larger
degree than what the state of the art models are. None of the mention models have deterministic
processing speeds | nb_NO |
