Video object detection and tracking algorithms have become the research focus in the field of computer vision. Traditional methods need to manually collect samples to train detection models

and build object detection and tracking systems. When the imaging conditions change

it is necessary to re-collect samples to train the detection model and re-adjust the entire detection and tracking system

which requires tedious human efforts. In this paper

a multi-object detection and tracking algorithm is proposed based on few-shot learning. With this approach

a hybrid classifier that models one object class can be generated by simply marking several bounding boxes around the object in the first video frame. An online gradual learning algorithm is proposed to learn the object pose changes and update the model. Combined with the color-based object tracking algorithm

our method automatically builds high-precision object detection and tracking systems without manual collection and labeling training samples. This approach can be conveniently replicated many times in different surveillance scenes and produce scene-specific detectors under various camera viewpoints. Experimental results on several video datasets show our approach achieves comparable performance to robust supervised methods

and outperforms the state-of-the-art online learning methods in varying imaging conditions.