In object detection networks, one widely used and effective approach to address the challenge of detecting small-sized objects in images is to employ multiscale pyramid features for prediction. Based on the fundamental principles of pyramid feature extraction, shallow features with small receptive fields are responsible for predicting small-sized objects, while deep features with large receptive fields handle large-sized objects. However, during the actual network training process using this structure, the loss function only provides the error between all positive samples and labels, treating them equally without considering the relationship between the actual size of the label and the feature layer where the sample resides, which to some extent affects the object detection performance. To address this, this paper proposes a novel method for constructing a loss function, termed Predicted Probability Loss (PP-Loss). It determines the probability of each feature layer predicting the objects labeled by the labels based on the size of the labels and uses this probability to adjust the weights of different sample anchors in the loss function, thereby guiding the network training. The prediction probability values for each feature layer are obtained from a prediction probability function established on a statistical basis. The algorithm has been experimentally validated on different networks with YOLO as the core. The results show that the convergence speed and accuracy of the network during training have been improved to varying degrees.

Video super-resolution reconstruction is the process of reconstructing low-resolution video frames into high-resolution video frames. Most of the current methods use motion estimation and motion compensation to extract temporal series information, but the inaccuracy of motion estimation will lead to the degradation of the quality of video super-resolution results. Additionally, when using convolution network to extract feature information, the number of feature information is limited by the number of feature channels, resulting in poor reconstruction results. In this paper, we propose a recurrent structure of regional focus network for video super-resolution, which can avoid the influence of inaccurate motion compensation on super-resolution results. Meanwhile, regional focus blocks in the network can focus on different areas of video frames, extract different features from shallow to deep layers, and skip-connect to the last layer of the network through feature aggregation to improve the richness of features participating in the reconstruction. The experimental results show that our method has higher computational efficiency and better video super-resolution results than other temporal modeling methods.

In this paper, we present a codebook-based background modeling method to detect moving objects from video sequences to adapt to some circumstance changes. The characteristic parameters and numbers of code vector of codebook for each pixel after studied are different, so the background description is more flexible and abundant. The test results show that this algorithm can handle scenes that contain moving background or illumination variation, and it achieves robust detection for compressed videos

This paper proposes a real-time moving objects detecting algorithm based on computer vision. Contour feature is firstly extracted from the initial frame as the feature to describe object, and then the moving object in the latter frame could be tracked and located by marking the location of this feature, at the same time, the feature is dynamically adaptively updated and optimized online. The slight computation burden and robustness to the noise and illumination disturbance make the algorithm have a good real-time quality and reliability. Experimental results indicate that the system reaches the designing requirements.