Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

• URL: http://arxiv.org/abs/2208.08220v1
• Date: Wed, 17 Aug 2022 11:02:29 GMT
• Title: Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System
• Authors: Tung-Lam Duong, Van-Duc Le, Tien-Cuong Bui, and Hai-Thien To
• Abstract summary: We propose an end-to-end smart camera parking system where we provide an autonomous detecting occupancy by an object detector called OcpDet. Our detector also provides meaningful information from contrastive modules: training and spatial knowledge, which avert false detections during inference. We benchmark OcpDet on the existing PKLot dataset and reach competitive results compared to traditional classification solutions.
• Score: 0.26249027950824505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system's scalability. Additionally, most of the approaches use deep learning models, making them not error-free and not reliable at scale. Thus, we propose an end-to-end smart camera parking system where we provide an autonomous detecting occupancy by an object detector called OcpDet. Our detector also provides meaningful information from contrastive modules: training and spatial knowledge, which avert false detections during inference. We benchmark OcpDet on the existing PKLot dataset and reach competitive results compared to traditional classification solutions. We also introduce an additional SNU-SPS dataset, in which we estimate the system performance from various views and conduct system evaluation in parking assignment tasks. The result from our dataset shows that our system is promising for real-world applications.

Related papers

• Smart Camera Parking System With Auto Parking Spot Detection [1.0512475026060208]
  We provide a novel approach called PakSta for identifying the state of parking spots automatically. Our method utilizes object detector from PakLoc to simultaneously determine the occupancy status of all parking lots within a video frame. The efficacy of our proposed methodology on the PKLot dataset results in a significant reduction in human labor of 94.25%.
  arXiv  Detail & Related papers  (2024-07-07T19:00:11Z)
• Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems [3.00363876980149]
  Intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images. We found that models trained on diverse datasets can achieve 95% accuracy without the burden of data annotation and model training on the target parking lot.
  arXiv  Detail & Related papers  (2023-09-28T14:59:53Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs [0.2519906683279152]
  We introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals.
  arXiv  Detail & Related papers  (2023-03-17T12:08:47Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Multimodal Detection of Unknown Objects on Roads for Autonomous Driving [4.3310896118860445]
  We propose a novel pipeline to detect unknown objects. We make use of lidar and camera data by combining state-of-the art detection models in a sequential manner.
  arXiv  Detail & Related papers  (2022-05-03T10:58:41Z)
• A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception [164.93739293097605]
  We propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms. We have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric.
  arXiv  Detail & Related papers  (2022-03-04T22:16:50Z)
• Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach [0.0]
  This paper investigates the use of dehazing networks that improves the performance of parking space occupancy under hazy conditions. The proposed system is deployable as part of existing smart parking systems where limited number of cameras are used to monitor hundreds of parking spaces.
  arXiv  Detail & Related papers  (2022-01-15T14:15:46Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Training-free Monocular 3D Event Detection System for Traffic Surveillance [93.65240041833319]
  Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. In real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible. We propose a training-free monocular 3D event detection system for traffic surveillance.
  arXiv  Detail & Related papers  (2020-02-01T04:42:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.