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Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security and surveillance etc. Generally, pedestrian detection is conducted for images in visible spectrum, which are not suitable for night time detection. Infrared (IR) or thermal imaging is often adopted for night time due to its capability of capturing the emitted energy from pedestrians. The detection process firstly extracts candidate pedestrians from the captured IR image. Robust feature descriptors are formulated to represent those candidates. A binary classification of the extract features is then performed with trained classifier models. In this paper, an algorithm for pedestrian detection from IR image is proposed, where an adaptive fuzzy C-means clustering and convolutional neural networks are adopted. The adaptive fuzzy C-means clustering is used to segment the IR images and retrieve the candidate pedestrians. The candidate pedestrians are then pruned using human posture characteristics and the second central moments ellipse. The convolutional neural network is used to simultaneously learn relevant features and perform the binary classification. The performance of the proposed algorithm is compared with state-of-the-art algorithms on publicly available data set. A better detection accuracy with reduced computational accuracy is achieved.

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Collaborative Filtering (CF) signals are crucial for a Recommender System (RS) model to learn user and item embeddings. High-order information can alleviate the cold-start issue of CF-based methods, which is modelled through propagating the information over the user-item bipartite graph. Recent Graph Neural Networks (GNNs) propose to stack multiple aggregation layers to propagate high-order signals. However, the oscillation problem, varying locality of bipartite graph, and the fix propagation pattern spoil the ability of multi-layer structure to propagate information. The oscillation problem results from the bipartite structure, as the information from users only propagates to items. Besides oscillation problem, varying locality suggests the density of nodes should be considered in the propagation process. Moreover, the layer-fixed propagation pattern introduces redundant information between layers. In order to tackle these problems, we propose a new RS model, named as Deoscillated Graph Collaborative Filtering (DGCF). We introduce crosshop propagation layers in it to break the bipartite propagating structure, thus resolving the oscillation problem. Additionally, we design innovative locality-adaptive layers which adaptively propagate information. Stacking multiple crosshop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers. Extensive experiments on real-world datasets show the effectiveness of DGCF. Detailed analyses indicate that DGCF solves oscillation problem, adaptively learns local factor, and has layer-wise propagation pattern. Our code is available online 1 . * Equal contributions Preprint. Under review 1 https://github.com/JimLiu96/DeosciRec