The GIST descriptor is a classic feature descriptor primarily used for scene categorization and recognition tasks. It drives a bank of Gabor filters, which respond to edges and textures at various scales and orientations to capture the spatial structures in an image. Compared to other scene recognition algorithms that rely on detailed object detection, GIST has lower computational complexity, allowing it to be widely applied. However, its internal multi-scale and multi-orientation Gabor filters also mean that systems based on it cannot be executed fast enough. This article proposes an optimized GPU kernel for the GIST descriptor. It fully takes advantage of the symmetry of Gabor filters and proposes different optimization strategies for both oblique and orthogonal orientations. Extensive experiments demonstrate that the proposed kernel is adaptable to images of various scales and different GPUs. Compared to the cuFFT library, our kernel achieves 12.09× and 3.86× acceleration on an RTX 3080 GPU and a Jetson AGX Xavier GPU, respectively.

Stereo vision, a popular depth estimation technology in computing vision, finds wide-ranging applications in embedded systems, including robotics vision and autonomous driving. These applications demand both high accuracy and fast processing speeds. To address hardware limitations, most current embedded systems rely on nonlearning algorithms for fast matching, sacrificing accuracy. Some recent studies have explored using convolutional neural networks (CNNs) to improve matching accuracy, but the computational load of existing learning-based systems hampers real-world applicability. This article presents significant contributions: 1) a novel stereo matching framework that greatly enhances accuracy on real-time embedded platforms and 2) a two-pronged approach combining a nonlearning-based algorithm and a lightweight super-resolution residual neural network (sRRNet). The nonlearning-based algorithm yields a low-resolution disparity map, while the lightweight sRRNet generates a high-resolution disparity map. Experimental results on benchmark data demonstrate that the proposed method achieves a low matching error rate of 5.17% and a real-time processing speed of 51 fps using the embedded Jetson AGX GPU. The proposed method outperforms all existing real-time embedded systems.

This paper deals with a combinatorial optimization problem that models real-time taxi-sharing services. Because finding an optimal solution to this problem is NP-hard, most previous studies have developed the corresponding optimization algorithms based on well-known metaheuristics for its simplified problem. In this study, we focus on assigning an appropriate taxi and re-planning its route for each newly arisen demand so that the service can minimize the sum of the planned travel time for transporting all passengers who are allocated to this taxi. We propose a novel algorithm based on incremental Boolean satisfiability solving, to optimize the taxi allocation for demands occurring in real time. In our experiment, we generate instances by simulating urban traffic based on a road network. The experimental result shows that our new approach is overall faster than its equivalent integer programming-based solving method.

A pedestrian road network is a complex non-linear dynamic system, and it is prone to severe accidents due to the congestion of spatiotemporal mass gathering. This paper proposes a framework of hybrid crowd dynamics modeling and model predictive control to ease the pressure of pedestrian road networks. The framework is integrated on the basis of a mature paradigm in the field of traffic signal control; the theory is yet new to pedestrian road networks except general traffic signals on the streets. The novelty also lies in its ability to simultaneously monitor both the discrete and continuous dynamics changes in the overall network. We verify the framework through comparison with simulation of manual control commands reproduced from real cases of a large-scale outdoor event. The results indicate that the proposed framework has a superior performance in keeping the balance in the overall distribution of large-scale crowd and reducing the risk of saturation in a pedestrian road network.

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z2- ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

In this study, we consider the multiple traveling salesmen problem (mTSP) with the min-max objective of minimizing the longest tour length. We begin by reviewing an existing integer programming (IP) formulation of this problem. Then, we present several novel conjunctive normal form (CNF) encodings and an approach based on modifying a maximum satisfiability (MaxSAT) algorithm for the min-max mTSP. The correctness and the space complexity of each encoding are analyzed. In our experiments, we compare the performance of solving the TSP benchmark instances using an existing encoding and our new encodings comparing the results achieved using an implemented group MaxSAT solver to those achieved using the IP method. The results show that for the same problem, the new encodings significantly reduce the number of generated clauses over the existing CNF encoding. Although the proposals are still not competitive compared to the IP method, one of them may be more effective on relatively large-scale problems, and it has an advantage over the IP method in solving an instance with a small ratio of the number of cities to the number of salesmen.

Many problems in various fields can be expressed as the problem of optimizing the value of a pseudo-Boolean constraint which is a linear constraint over Boolean variables. This paper proposes a new technique, called Mixed Radix Weight Totalizer Encoding (MRWTE), which encodes pseudo-Boolean constraints into clauses that can be handled by a standard SAT solver. This new technique will allow us to fully exploit the latest improvements in SAT research. Unlike other encodings, the number of auxiliary variables required for MRWTE does not depend on the magnitudes of the coefficients. Instead, it depends on the number of distinct combinations of the coefficients. Our experimental results show that MRWTE compactly encodes the constraints, and the obtained clauses are efficiently handled by a state-of-the-art SAT solver.