Adaptive Learning Based on Tracking and ReIdentifying Objects Using Convolutional Neural Network

This paper proposes a solution based on Adaptive learning using the CNN model. The proposed method automatically updates the recognition model according to online training dataset accumulated directly from the system and retraining recognition model. The data updating task focuses on data samples that are less similar to previous trained ones. The purpose of this solution is to upgrade the model to a new one more adaptive, expecting to reach higher accuracy. In the adaptive learning approach, the recognition system is capable of self-learning and complementing data, without experts needed for data labeling or training. The proposed solution includes 5 main phases: (1) Detect and recognize low confident objects; (2) Track objects in n frames in future progress to make sure whether they are interesting objects or not. (3) In case of objects that are recognized with high confidence: labeling (same class of object) for the corresponding data samples to be recognized with low confidence scores which were tracked in the previous process. In case of objects determined not to be of interesting objects, the samples are labeled as Negative for all previous samples, which were tracked in n previous frames; (4) Initialize a training dataset based on a selective combination of previously trained data and the new data. (5) Retrain and update the model if it results in higher accuracy. We have conducted experiments to compare results of the proposed model—PDnet and some state of the art methods such as AlexNet and Vgg. The experimental results demonstrate that the proposed method provides the higher accuracy when the model are self-learned over time. On the other hand, our adaptive learning is applicable to the traditional recognition models such as AlexNet and Vgg model for improving accuracy.