The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.

Fox News host Sean Hannity calls out Vice President Kamala Harris' far-left policies ahead of the November election on'Hannity.' Over the last year, Fox News correspondents Bill Melguin and Griff Jenkins have been following complaints from Border Patrol sources that many of the crucial remote surveillance cameras in multiple sectors along the southern border have not been operational. U.S. House of Representatives Homeland Security Committee Republicans say whistleblowers came forward to the committee last week, claiming that "some of the busiest Southwest border sectors have nearly 50 or more cameras offline with multiple towers that have been out of service for more than a year." On Wednesday, the House Homeland Security Committee sent a letter to Department of Homeland Security (DHS) Secretary Alejandro Mayorkas, claiming that whistleblowers came forward to the committee last week with concerning information on this issue. The letter from Republicans to Mayorkas demanded answers.

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

Fox News correspondent Stephanie Bennett joins'Fox News Live' to break down recent evidence tying missile fragments in Russian attacks to North Korea. North Korea is putting surveillance cameras in schools and workplaces and collecting fingerprints, photographs and other biometric information from its citizens in a technology-driven push to monitor its population even more closely, a report said Tuesday. The state's growing use of digital surveillance tools, which combine equipment imported from China with domestically developed software, threatens to erase many of the small spaces North Koreans have left to engage in private business activities, access foreign media and secretly criticize their government, the researchers wrote. But the isolated country's digital ambitions have to contend with poor electricity supplies and low network connectivity. Those challenges, and a history of reliance on human methods of spying on its citizens, mean that digital surveillance isn't yet as pervasive as in China, according to the report, published by the North Korea-focused website 38 North. The study's findings align with widely held views that North Korean leader Kim Jong Un is stepping up efforts to tighten the state's control of its citizens and promote loyalty to his regime.

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.

Collecting traffic data is crucial for transportation systems and urban planning, and is often more desirable through easy-to-deploy but power-constrained devices, due to the unavailability or high cost of power and network infrastructure. The limited power means an inevitable trade-off between data collection duration and accuracy/resolution. We introduce a novel learning-based framework that strategically decides observation timings for battery-powered devices and reconstructs the full data stream from sparsely sampled observations, resulting in minimal performance loss and a significantly prolonged system lifetime. Our framework comprises a predictor, a controller, and an estimator. The predictor utilizes historical data to forecast future trends within a fixed time horizon. The controller uses the forecasts to determine the next optimal timing for data collection. Finally, the estimator reconstructs the complete data profile from the sampled observations. We evaluate the performance of the proposed method on PeMS data by an RNN (Recurrent Neural Network) predictor and estimator, and a DRQN (Deep Recurrent Q-Network) controller, and compare it against the baseline that uses Kalman filter and uniform sampling. The results indicate that our method outperforms the baseline, primarily due to the inclusion of more representative data points in the profile, resulting in an overall 10\% improvement in estimation accuracy. Source code will be publicly available.

Traffic cameras remain the primary source data for surveillance activities such as congestion and incident monitoring. To date, State agencies continue to rely on manual effort to extract data from networked cameras due to limitations of the current automatic vision systems including requirements for complex camera calibration and inability to generate high resolution data. This study implements a three-stage video analytics framework for extracting high-resolution traffic data such vehicle counts, speed, and acceleration from infrastructure-mounted CCTV cameras. The key components of the framework include object recognition, perspective transformation, and vehicle trajectory reconstruction for traffic data collection. First, a state-of-the-art vehicle recognition model is implemented to detect and classify vehicles. Next, to correct for camera distortion and reduce partial occlusion, an algorithm inspired by two-point linear perspective is utilized to extracts the region of interest (ROI) automatically, while a 2D homography technique transforms the CCTV view to bird's-eye view (BEV). Cameras are calibrated with a two-layer matrix system to enable the extraction of speed and acceleration by converting image coordinates to real-world measurements. Individual vehicle trajectories are constructed and compared in BEV using two time-space-feature-based object trackers, namely Motpy and BYTETrack. The results of the current study showed about +/- 4.5% error rate for directional traffic counts, less than 10% MSE for speed bias between camera estimates in comparison to estimates from probe data sources. Extracting high-resolution data from traffic cameras has several implications, ranging from improvements in traffic management and identify dangerous driving behavior, high-risk areas for accidents, and other safety concerns, enabling proactive measures to reduce accidents and fatalities.

After the bus driver ordered him to observe a rule requiring passengers to wear face masks, he approached the fare box and began arguing with her. "I hit bitches," he said, leaning over a plastic shield that the driver was sitting behind. When she pulled out her iPhone to call the police, he reached around the shield, snatched the device, and raced off. The bus driver followed the man outside, where he punched her in the face repeatedly. He then stood by the curb, laughing, as his victim wiped blood from her nose. By the time police officers canvassed the area, the assailant had fled, but the incident had been captured on surveillance cameras.

Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camera-based system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for real-time notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities' privacy and ethical requirements as well as hardware configuration and setups. We evaluate the system's performance using throughput and end-to-end latency. The experiment results show that, on average, our system's end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate.