# Person Search via Deep Integrated Networks
## 1 Introduction
The person search method has recently been introduced to close the gap. Person search is more challenging than person ReID because of the imprecise detection cropping and misalignment [8; 9]. In 2017, [PERSON] et al. [9] proposed a deep learning framework to jointly optimize pedestrian detection and person ReID in a single convolutional neural network (CNN). An online instance matching loss function has been proposed to train the network. [PERSON] et al. [39] proposed a neural search model to recursively refine the location of the target person in the scene. In 2018, [PERSON] et al. [8] discovered that auto-detected bounding boxes often vary from traditional benchmarks in scale (resolution). Accordingly, they proposed a cross-level semantic alignment to address the multi-scale matching problem in person search. In this study, we designed a framework for practical surveillance scenarios in which the scene images captured different viewpoints and regions, and the pedestrian matching is automatically performed in these images. Pedestrian detection is a necessary step before person matching. The computational efficiency could be improved if the extracted features in the detection step could be further used in the following ReID process. Moreover, the image variances are considered by training the ReID model from the auto-detected bounding boxes, which will increase the model tolerance in the test process. We proposed an integrated deep network consisting of a detection and identification module. Two scene images are input to the detection network. The detection module is based on the Faster R-CNN [35], which can provide a more precise bounding box. The person regions are detected and cropped. Each cropped region is further input to the identification module. In previous studies [13; 15; 20; 40; 41], person ReID was cast as a two-class classification problem, and each training pair was given the same or different class label if the two images were from the same person or not. Instead of training the identification module as a two-class classification, we apply a multi-class convolutional neural network (CNN) network to train the identification module, in which each person is considered as a class and an identity (ID) is assigned. The amount of training data for each class is almost the same. The discriminative features are then extracted in the identification module [11]. Finally, the person matching is performed by computing the similarity between the probe image and the gallery images based on the learned distance metric. After sorting the similarities, the top-\(k\) results (\(k\) is a user-defined value) are shown. Note that the ideas of the proposed system not only add a detection module before identification, but the use of shared features is considered in the system design to improve the computational efficiency. Moreover, in a practical scenario, it would not be feasible to retrain the network when a newcomer's images are captured. Hence, a flexible system framework was designed by training a multi-class CNN for discriminative feature extraction and learning a distance metric for similarity measurement. The remainder of this paper is organised as follows: Section 2 reviews the issues of person re-identification; Section 3 presents the proposed system consisting of two modules, person detection and person re-identification, with the details of each module also introduced; Section 4 presents the network parameters and experimental results of the proposed system and discusses the performance with various feature vectors; and finally, Section 5 concludes the paper.