BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities

Collaborative perception enables agents to share complementary perceptual information with nearby agents. This would improve the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. Most existing approaches mainly focus on single modality (especially LiDAR), and not fully exploit the superiority of multi-modal perception. We propose a collaborative perception paradigm, BM2CP, which employs LiDAR and camera to achieve efficient multi-modal perception. It utilizes LiDAR-guided modal fusion, cooperative depth generation and modality-guided intermediate fusion to acquire deep interactions among modalities of different agents, Moreover, it is capable to cope with the special case where one of the sensors, same or different type, of any agent is missing. Extensive experiments validate that our approach outperforms the state-of-the-art methods with 50X lower communication volumes in both simulated and real-world autonomous driving scenarios. Our code is available at https://github.com/byzhaoAI/BM2CP.Comment: 14 pages, 8 figures. Accepted by CoRL 202

Machine vision and the OMV

The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described

ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making  it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas. The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues

Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video

Spatial-temporal Map (STMap)-based methods have shown great potential to process high-angle videos for vehicle trajectory reconstruction, which can meet the needs of various data-driven modeling and imitation learning applications. In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that imposes parity constraints at both pixel and instance levels to generate instance-aware embeddings for vehicle stripe segmentation on STMap. At pixel level, each pixel was encoded with its 8-neighbor pixels at different ranges, and this encoding is subsequently used to guide a neural network to learn the embedding mechanism. At the instance level, a discriminative loss function is designed to pull pixels belonging to the same instance closer and separate the mean value of different instances far apart in the embedding space. The output of the spatial-temporal affinity is then optimized by the mutex-watershed algorithm to obtain final clustering results. Based on segmentation metrics, our model outperformed five other baselines that have been used for STMap processing and shows robustness under the influence of shadows, static noises, and overlapping. The designed model is applied to process all public NGSIM US-101 videos to generate complete vehicle trajectories, indicating a good scalability and adaptability. Last but not least, the strengths of the scanline method with STDE and future directions were discussed. Code, STMap dataset and video trajectory are made publicly available in the online repository. GitHub Link: shorturl.at/jklT0

Teachers in concordance for pseudo-labeling of 3D sequential data

Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is specially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and manual labeling is very challenging. We propose to leverage sequences of point clouds to boost the pseudolabeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information. This set of teachers, dubbed Concordance, provides higher quality pseudo-labels for student training than standard methods. The output of multiple teachers is combined via a novel pseudo label confidence-guided criterion. Our experimental evaluation focuses on the 3D point cloud domain and urban driving scenarios. We show the performance of our method applied to 3D semantic segmentation and 3D object detection on three benchmark datasets. Our approach, which uses only 20% manual labels, outperforms some fully supervised methods. A notable performance boost is achieved for classes rarely appearing in training data.Comment: This work has been submitted to the IEEE for publicatio

Resource saving Approach of visual tracking fiducial marker recognition for unmanned aerial vehicle

Unmanned aerial vehicle (UAV) tracking fiducial marker is a challenging problem, because of camera system vibration, which causes visible frame-to-frame jitter in the airborne videos and unclear marker vision. Multirotors have very limited weight carrying, controller, and battery power resources. While obtaining and processing motion blurred images, which have no useful information, requires much more image processing subsystem resources. The paper presents blurry image frame elimination based approach of UAV resource saving fiducial marker visual tracking. The proposed approach integrates accelerometer and visual data processing algorithms to predict image blur and skip blurred frames. Experiments have been performed to verify the validity of the proposed approach