Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System.

Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360° horizontal and 90° vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved.

Environmental microorganism classification using optimized deep learning model.

Rapid environmental microorganism (EM) classification under microscopic images would help considerably identify water quality. Because of the development of artificial intelligence, a deep convolutional neural network (CNN) has become a major solution for image classification. Three popular CNNs, referred to as ResNet50, Vgg16, and Inception-v3, were transferred to identify the EM images present on the Environmental Microorganism Dataset (EMDS), and EMAD was the small dataset, which only has 294 EM images with 21 EM classes. Besides data augmentation, optimizing the fully connected layer of CNN, i.e., both optimally fine-tuned neuron number and dropout rate, was adopted to enhance the performance produced by CNN. The discussions on the causes of the accuracy improved by optimization are also provided. The results showed that the Inception-v3 model obtained 84.9% of the accuracy and performed better than the other two famous CNNs. Also, the implement of data augmentation enhanced the performance of Inception-v3 on EMDS. To add to that, the optimized Inception-v3 model archived 90.5% of the accuracy, and this result demonstrated the improvement effect obtained by using genetic algorithm (GA) to optimize the fully connected layer of the Inception-v3. Therefore, the optimize Inception-v3 with data augmentation process obtained the accuracy of 92.9% and improved almost 21% higher than that obtained from the famous Vgg16. In addition, the optimized Inception-v3 would need less neurons, when compared with that of the optimized Vgg16 possibly. This optimized Inception-v3 could provide a solution to the EM classification in microscope with a digital camera system.