# Person Search via Deep Integrated Networks
## 1 Introduction  
With the rapid development of information technology, security and surveillance systems have been installed in many places, including schools, department stores, train stations, airports, office buildings and so on, for public safety. Cameras are usually installed with non-overlap views to increase the security area and strike a balance between equipment cost and security. From the view of the data mining field, surveillance intelligence can be seen from the sequential data (videos) [1; 2; 3] and abnormal or specific patterns can be found; e.g., vehicle, pedestrian. Several subjects (e.g., abnormal event detection, object detection, tracking, face recognition, and person re-identification) have been explored in computer vision [4; 5; 6]. Person search plays an important role in the intelligent surveillance system, which was first introduced in [7]. [PERSON] et al. [7] proposed a sliding window searching strategy based on person detection and person matching scores to find a person's image captured by one camera (i.e., probe image) in a gallery of scene images [8; 9], where probe and gallery images are captured with different viewpoints. Person search is an extended form of person re-identification (person ReID) and is designed to find a probe person in a gallery of scene images. Large visual variations caused by changes inillumination, occlusion, background clutter, different viewpoints, and human poses pose a challenging problem [8; 9]. Large intra-class variations, which include different appearances, increase the difficulty of feature representation. Besides this, the class imbalance problem is a problem in machine learning; i.e., the amount of data for one class is far less than the amount of data for another class [10]. Person search has the imbalance problem of having a set of \"different\" pairs (i.e., a combination of two images from different persons) while having a limited set of \"same\" pairs (i.e., a combination of two images from the same person). The similarity measurement is therefore difficult to learn, and data sampling techniques are often utilized during the training process [11; 12]. Existing research can be roughly divided into two categories for person re-identification. One extracts the discriminative feature for a person's image [13; 14], while the other learns the metric with a form of a matrix to measure the similarity [15] between persons' images. With the great success achieved by the deep learning network in computer vision [16; 17; 18], some works have recently been proposed to address person ReID with deep learning networks in an end-to-end architecture, which refers to giving input data and, with the desired output label, a system is automatically trained as a whole [19]. Three kinds of deep models are used in person ReID: Siamese networks [13; 15; 20], classification networks [21; 22; 23], and triplet networks [24; 25]. Additionally, many works have focused on coping with specific issues, such as occlusion [26], misalignment [27; 28; 29] and over-fitting [19; 23; 30; 31]. Although numerous works have been proposed for person ReID, the gallery or probe pedestrian images are manually cropped in most benchmarks [13; 32; 33; 34]. Following the protocols of these benchmarks, most existing works have taken cropped pedestrian images either from manual labelling or a perfect detection assumption [9]. However, in practical applications, manual processing is time consuming and unavailable, thereby causing a gap between person ReID and practical applications [9]. Although the performance of pedestrian detection by deep learning models such as the Faster R-CNN [35], Single Shot MultiBox Detector (SSD) [36] and Person of Interest [37] is significantly better than the traditional method [38], imperfect bounding boxes, background clutter, misdetections, misalignment or false alarms are inevitable. In other words, the detection results affect the person ReID; however, most studies consider pedestrian detection and person ReID as separate issues [9].