This paper designs and implements a head-mounted individual monitoring and finding system, called iHead, to achieve proactive individual safety and assistance services through Internet of Things (IoT) technologies. The iHead system utilizes inertial data to monitor individual behavior and detects incidents (e.g., falls, crashing accidents, head hitting, etc.) based on deep learning. The individual activity recognition model is developed based on heterogeneous learning attention of long short-term memory, feedforward neural networks, and densely connected neural networks to accurately identify individual status. In addition, in daily use, iHead can recognize bad sitting posture and provide suggestions for correcting the sitting posture. Furthermore, in hazardous situations, iHead can automatically issue alerts and actively track the target individual in a crowdsourced sensing manner. To validate the feasibility of iHead, an Android-based prototype with the head wearable device for individual monitoring and finding has been implemented.

Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our goal is to train a model with a ‘unified’ architecture and only one set of pretrained weights that can tackle multiple types of adverse weathers such as haze, snow, and rain simultaneously. To this end, a two-stage knowledge learning mechanism including knowledge collation (KC) and knowledge examination (KE) based on a multi-teacher and student architecture is proposed. At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem. To accomplish this process, a novel collaborative knowledge transfer is proposed. At the KE, the student model is trained without the teacher networks and examined by challenging pixel loss derived by the ground truth. Moreover, to improve the performance of our training framework, a novel loss function called multi-contrastive knowledge regularization (MCR) loss is proposed. Experiments on several datasets show that our student model can achieve promising results on different bad weather removal tasks simultaneously. The code is available in our project page.

Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive feature fusion network (LAFFNet). The model is the encoder-decoder model with multiple adaptive feature fusion (AAF) modules. AAF subsumes multiple branches with different kernel sizes to generate multi-scale feature maps. Furthermore, channel attention is used to merge these feature maps adaptively. Our method reduces the number of parameters from 2.5M to 0.15M (around 94% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our LAFFNet effectively improves high-level vision tasks like salience object detection and single image depth estimation.

Modeling induction motor dynamics is a crucial problem in the industry. The previous works mainly model the dynamics based on the physical model assumption and state equation. However, due to the complex internal structure of motors, the traditional methods cannot estimate dynamics precisely. To address this issue, we adopt a deep learning-based approach that takes the time-series motor data measured from the sensor to estimate the dynamics without making any assumptions about the motor’s interior. In this paper, we propose a multi-scale feature aggregation with self-attention network (MASNet) to deal with modeling motor dynamics. First, our model extracts multi-scale features from motor signals by various convolutional kernel sizes. Then, the proposed adaptive feature aggregation module fuses different receptive signals effectively. To further refine these high-level motor features, two-stream components, containing Bidirectional LSTM and self-attention module, are applied to improve motor contextual information. Moreover, we present a novel temporal relative loss to enhance consecutive signal consistency, which improves the performance of modeling dynamics. To deploy the service in the real-world scenario, our network is very lightweight and reduces the number of parameters from 0.62M to 0.09M (around 85% reduction) but outperforms state-of-the-art algorithms by extensive experiments on simulation and real-world motor datasets.

Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net.

Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillance systems and intelligent vehicles. In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network. Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales. Furthermore, feature maps are integrated by channel attention. Our proposed network learns confidence maps of four sub-band images derived from the wavelet transform of the original images. Finally, the clear image can be well restored via the wavelet reconstruction and fusion of the low-frequency part and high-frequency parts. Several experimental results on synthetic and real images present that the proposed algorithm outperforms state-of-the-art methods.

Deep learning has played an important role in various computer vision tasks, and so does in single image dehazing. We think edges and colors are two key factors to obtain better dehazed images. Clear edges and balanced color make the dehazed images look natural and detailed. Therefore, we establish a novel two-stage and end-to-end network that comprises two characteristics. First, we propose the wavelet U-net, which uses discrete wavelet transform and inverse discrete wavelet transform to replace down-sampling and up-sampling, to extract edge features. Second, we apply the chromatic adaptation transform which can be implemented by convolutional layers mathematically to enhance images. Our neural network combining two concepts outperforms the state-of-art algorithms in visual performance and quantitative aspects.