Optimization methods of video images processing for mobile object recognition

Recognition of moving objects in video images is mainly based on acquiring the target information in a certain time series. After image processing, relevant algorithms are used to get the internal features and effectively identify the target object. However, image background, noise, definition and other factors will have impacts on mobile object recognition. Therefore, the mobile objects in video images are more complicated than the static objects in the fixed images. The traditional convolutional neural network (CNN) uses gradient descent algorithm for learning and training, and uses gradient descent algorithm to determine the initial thresholds, weights, which may cause the training to fall into a local optimal state. Therefore, this paper proposes an improved adaptive genetic algorithm combined with CNN. The thresholds and weights of CNN can be optimized by using adaptive genetic algorithm (AGA), which can overcome the shortcomings of the original genetic algorithm such as slow convergence. Experimental results shows that the recognition accuracy rate of the experiment increased from 83.75% to 92%, the method can effectively improve the accuracy and efficiency of mobile object recognition.