# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 1 Introduction  
Person re-identification (re-ID) technology aims to match (associate) an individual identity in non-overlapping physical locations or time, which has become a vital role for maintaining the public security in the fields of smart cities and artificial intelligence [1]. Owing to its superior performance in tracking the suspects and identifying their status, it has been widely applied to underpin many crucial applications such as criminal investigation [1], action recognition [2], and object tracking [3]. Given a query image of the person taken from one camera view, re-ID is essentially an image retrieval problem that is devoted to searching the images of the same person captured by other camera views and generates a final ranking list where top-ranked results are more likely to be the same person as the queried one [1]. However, owing to great variations in illumination, camera views, and the images' resolution and pose, the individual's appearance usually exhibits dramatic changes in harsh real-world scenarios (see Figure 1). As a result, it inevitably leads to an unstable performance for person re-ID in the real applications, which remains an unsolved and challenging problem. Recent booming deep-learning algorithms have significantly enhanced the performance of various computer vision tasks [4]. Person re-ID based on deep-learning has also become prevalent [4; 5]. However, deep learning models usually demand significant computing resources for optimization of numerous parameters and the very complex networks. This inevitably causes a performance bottleneck in real-time applications where trade-offs between recognition accuracy and efficiency are often a must [6]. In addition, due to the chained training criterion of existing deep modules, information-forward propagation is prone to gradient dispersion when confronting the deeper and deeper network architectures [7]. The primary input information gradually weakens during forward propagation and the model converges very slowly after a few layers [8]. In view of the network architecture, the middle layer of neural networks still retains the multiple-attribute information (such as color or body-region information) of the person [9]. It is very difficult to ensure the robustness and discrimination of the person's features without properly optimizing the network. A pressing need exists for a method to generate robustness and features to achieve accurate and efficient person re-ID [10; 11]. This study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture (Section 3): (1) The accessory attribute network (a-Net) grasps the person ornament information for an accessory descriptor by dividing the person appearance into the several regions of interests; (2) the sub-networks b-Net and c-Net are engaged in learning body and color descriptors; the b-Net captures the person region structure for a body descriptor (e.g., body structure) and the c-Net captures the color descriptor that maintains the consistency of a person's color; (3) multi-attribute descriptor: these descriptors use a tree-based feature-selection method to construct a global person feature inspired by the visual processing mechanism of human beings, which serves as the key to depict the person representation. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR (Section 4). Furthermore, the lightweight framework incurs a computational complexity as low as \(O(n)\). It takes less than 3 s to identify 5000 individuals with a HuaWei smart Table PC. Figure 1: The real surveillance scenes for the person re-ID application. There are multiple camera views for the same person with different representations that are in great variations in illumination (camera C), scales (camera A), and person poses (camera B) in harsh real-world scenarios.