Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset.

Although excessive proposals using traditional sliding-window methods or prevailing anchor-based techniques have been proposed to deal with deep learning-based pedestrian detection, it is still a promising yet challenging problem. In this paper, we propose a precise, flexible and thoroughly anchor-free, as well as proposal-free framework named Pedestrian-as-Points Network (PP-Net) for pedestrian detection. Specifically, we model a pedestrian as a single point, i.e., the center point of the instance, and predict the pedestrian scale at each detected center point. In order to achieve higher accuracy, we build a pyramid-like structure based on the backbone as a feature extractor to aggregate multi-level information. In addition, we construct a deep guidance module (DGM) at the top of the backbone, so that the higher-level information can be captured in the process of building a feature pyramid network (FPN) to avoid the dilution of high-level information on the top-down pathway. We further design a feature fusion unit (FFU) to fuse the fine-level features well with the coarse-level semantic information from the top-down pathway. With the only post-processing non-maximum suppression (NMS), we achieve better performance than many state-of-the-arts methods on the challenging pedestrian detection datasets.

Hashing technique has been extensively utilized in approximate nearest neighbor (ANN) search for large-scale image retrieval by virtue of its storage simplicity and computational efficiency. Recently, many researches show that hashing methods based. on deep neural networks (DNNs) can improve retrieval accuracy by simultaneously learning both deep feature representation and hashing functions in an end-to-end framework. Most deep supervised hashing methods aim to preserve the distance or similarity between data points using the similarity relationships constructed based on semantic labels of images, while ignoring the classification ability of the generated hash codes. However, the semantic labels themselves carry more information than the corresponding similarity labels. We propose an Improved. Pairwise-based Deep Hashing (IPDH) method to generate hash codes with powerful classification ability by exploring the global distribution of semantic labels. Specifically, the proposed IPDH method aims to minimize the information loss generated during the process of classification prediction to ensure that the output predicted labels of the network model has a similar distribution with those from the original semantic labels. Comprehensive experiments show that the proposed IPDH method can obtain better improvement than other state-of-the-art algorithms.