DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.Comment: CVPR 201

Two-Stream Contextualized CNN for Fine-Grained Image Classification

Human's cognition system prompts that context information provides potentially powerful clue while recognizing objects. However, for fine-grained image classification, the contribution of context may vary over different images, and sometimes the context even confuses the classification result. To alleviate this problem, in our work, we develop a novel approach, two-stream contextualized Convolutional Neural Network, which provides a simple but efficient context-content joint classification model under deep learning framework. The network merely requires the raw image and a coarse segmentation as input to extract both content and context features without need of human interaction. Moreover, our network adopts a weighted fusion scheme to combine the content and the context classifiers, while a subnetwork is introduced to adaptively determine the weight for each image. According to our experiments on public datasets, our approach achieves considerable high recognition accuracy without any tedious human's involvements, as compared with the state-of-the-art approaches

Infrared patch-image model for small target detection in a single image

The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods

Correction: Riaz et al. Traffic Anomaly Prediction System Using Predictive Network. <i>Remote Sens.</i> 2022, <i>14</i>, 447

Since the article “Traffic Anomaly Prediction System Using Predictive Network” by Riaz et al. [...