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Abstract The Wi‐Fi‐based human activity recognition shows immense potential, as it is device‐free, non‐intrusive to privacy, and low‐cost. However, current learning‐based recognition methods mostly adopt the hybrid representation without distinguished contributions of features to different activities, which will be seriously affected by environment variations and interference of other persons, and costly to extend to new activities. Therefore, this paper proposes HR‐HAR, a hierarchical relation representation for human activity recognition, to improve the performance, extensibility, and robustness by exploiting the hierarchical relation of features of activities. The hierarchical relation reflects the different contributions of features to recognize different activities and effectively distinguishes similar activities. It naturally leads to a layered structure that can be extended to new activities without re‐training the entire model. With the layered structure, HR‐HAR first detects the existence of other persons and then processes un‐interfered scene and interfered scene signals with different methods, so it is robust to the interference. The experimental results on the public dataset with 95.6% accuracy and on the self‐collected dataset with 95.4% accuracy for un‐interfered scene and 95.0% for interfered scene indicate that HR‐HAR is of reliable performance on human activity recognition and is robust to environmental changes and interference of other persons.

Person re-identification has become a challenging task due to various factors. One key to effective person re-identification is the extraction of the discriminative features of a person's appearance. Most previous works based on deep learning extract pedestrian characteristics from neural networks but only from the top feature layer. However, the low-layer feature could be more discriminative in certain circumstances. Hence, we propose a method, named the multi-level feature network with multiple losses (MFML), which has a multi-branch network architecture that consists of multiple middle layers and one top layer for feature representations. To extract the discriminative middle-layer features and have a good effect on deeper layers, we utilize the triplet loss function to train the middle-layer features. For the top layer, we focus on learning more discriminative feature representations, so we utilize the hybrid loss (HL) function to train the top-layer feature. Instead of concatenating multilayer features directly, we concatenate the weighted middle-layer features and the weighted top-layer feature as the discriminative features in the testing phase. The extensive evaluations conducted on three datasets show that our method achieves a competitive accuracy level compared with the state-of-the-art methods.

Visibility of outdoor scenes is often degraded by haze. The atmosphere particles absorb and scatter the light, causing failure in various computer vision applications. In this paper, we propose a naturalness preserved fast dehazing algorithm using hue, saturation, and value (HSV) color space. First, we process hazy images in HSV color space instead of red, green, and blue (RBG) in order to preserve hue and reduce computational complexity. Second, we use the modified morphological opening operation for estimating the transmission map. In this way, the halo effects are greatly suppressed, and it costs less time than the specially designed filters. The experimental results demonstrate that the proposed algorithm can effectively remove haze. Also, our algorithm maintains naturalness by preserving hue and suppressing halo effects. Moreover, the computational complexity has been largely reduced, thus making our algorithm appropriate for real-time applications.

The pedestrian attribute recognition aims to generate a structured description of pedestrians, which serves an important role in surveillance. Current works usually assume that the images and the specific pedestrian states, including pedestrian occlusion and pedestrian orientation, are given. However, we argue that the current works ignore the guidance of the pedestrian state and cannot achieve the appropriate performance since the appearance feature will become unreliable due to the variance of the pedestrian state, which is common in practice. Therefore, this paper proposes the Explicit State Representation (ExSR) Guided Pedestrian Attribute Recognition to improve the accuracy through state learning and attribute fusion among frames. Firstly, the pedestrian state is explicitly represented by concatenating the pedestrian orientation and occlusion, which can be accurately determined via analyzing the pose. Secondly, the state-aware pedestrian attribute fusion method is proposed and divided into two cases, namely the inter-state case and the intra-state case. In the intra-state case, the appearance feature will remain stable and the attribute relations are propagated to refine. The method of exploiting attribute relations within a single frame is the Graph Neural Network. In the inter-state case, the state changes, the attribute relationship propagation is prevented, and the advantages of attribute recognition in each frame are complemented to make a reliable judgment on the invisible region. The experimental results demonstrate that the ExSR outperforms the state-of-the-art methods on two public databases, benefiting from the explicit introduction of the state into the attribute recognition. [ABSTRACT FROM AUTHOR]