Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous computer vision tasks. In this paper, we present a new real-time multi-task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer — the prevalent issue in multi-task learning — we introduce a task-adaptive attention generator. This generator is designed to automatically discern interrelations across the three tasks and arrange the task-sharing pattern, all while leveraging the efficiency of the hard-parameter sharing approach. To the best of our knowledge, the proposed model is pioneering in its capability to concurrently handle multiple tasks, notably 3D object detection, while maintaining real-time processing speeds. Our rigorously optimized network, when tested on the Cityscapes-3D datasets, consistently outperforms various base-line models. Moreover, an in-depth ablation study substantiates the efficacy of the methodologies integrated into our framework.

In this paper, we propose a blind text image deblurring algorithm by using a text-specific hybrid dictionary. After careful analysis, we find that the text-specific hybrid dictionary has the great ability of providing powerful contextual information for text image deblurring. Here, it is worth noting that our proposed method is inspired by our observation that an intermediate latent image contains not only sharp regions, but also multiple types of small blurred regions. Based upon our discovery, we propose a prior for text images based on sparse representation, which models the relationship between an intermediate latent image and a desired sharp image. To this end, we carefully collect three different image patch pairs, which are 1) Gaussian blur-sharp, 2) motion blur-sharp, and 3) sharp-sharp, in order to construct the text-specific hybrid dictionary. We also propose a new optimization framework suitable for the task of text image deblurring in this paper. Extensive experiments have been conducted on a challenging dataset of synthetic and real-world text images. Our results demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods both quantitatively and qualitatively.

Heart rate (HR) is an important indicator of human health status. Traditional heart rate measurement methods rely on contact-based sensors or electrodes, which are inconvenient and troublesome for users. Remote sensing of the photoplephysmography (PPG) signal using a video camera provides a promising means to monitor vital signs of people without the need of any physical contact. However, until recently, most of the literature papers approaching this problem have only reported results from off-line recording videos taken under well controlled environments. In this paper, we propose a method to improve HR measurement accuracy under challenging environments involving factors such as subjects movement, complicated facial models (i.e., hair, glass, beards, etc.), subjects' distance to camera, and low illumination condition. We also build a framework for real-time measuring system and construct a stable model for recording and displaying results for long term heart rate monitoring. We tested our system on challenging dataset, and demonstrated that our method not only deals with real-time, on-line measurement tasks, but also outperforms others' works.

Estimating both defocus blur and motion blur regions from a single monocular image is a challenging research area in modern image processing and computer vision. Most existing algorithms first divide an image into either non-blur or blur patches. Then blur-type classification is performed on the blur patches only. This means that such approaches include potential risk that incorrect blur region identification may affect the following blur-type classification. In this paper, we present a novel framework for blur region identification to overcome the deficiency of classical methods. We propose a 3-way blur identification method, which divides an image into non-blur, defocus blur, and motion blur regions at once. To this end, we employ intuitive and powerful features based on specific criteria well-suited for our 3-way classification problem. We also take a coarse-to-fine technique to produce pixelwise segmentation results. Experimental results demonstrate that our proposed method outperforms the recent algorithms.