From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets

• URL: http://arxiv.org/abs/2205.12579v1
• Date: Wed, 25 May 2022 08:40:38 GMT
• Title: From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets
• Authors: Ross Greer and Mohan Trivedi
• Abstract summary: In this work, we contribute an EM algorithm for estimation of corner points and linear crossing segments for marked and unmarked pedestrian crosswalks using the detections of pedestrians from processed LiDAR point clouds or camera images. We demonstrate the algorithmic performance by analyzing three real-world datasets containing multiple periods of data collection for four-corner and two-corner intersections with marked and unmarked crosswalks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we contribute an EM algorithm for estimation of corner points and linear crossing segments for both marked and unmarked pedestrian crosswalks using the detections of pedestrians from processed LiDAR point clouds or camera images. We demonstrate the algorithmic performance by analyzing three real-world datasets containing multiple periods of data collection for four-corner and two-corner intersections with marked and unmarked crosswalks. Additionally, we include a Python video tool to visualize the crossing parameter estimation, pedestrian trajectories, and phase intervals in our public source code.

Related papers

• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection [12.145321599949236]
  We propose a new scheme for multi-task satellite imagery road extraction, Patch-wise Road Keypoints Detection (PaRK-Detect) Our framework predicts the position of patch-wise road keypoints and the adjacent relationships between them to construct road graphs in a single pass. We evaluate our approach against the existing state-of-the-art methods on DeepGlobe, Massachusetts Roads, and RoadTracer datasets and achieve competitive or better results.
  arXiv  Detail & Related papers  (2023-02-26T08:26:26Z)
• DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes [74.64897845999677]
  We introduce a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT.
  arXiv  Detail & Related papers  (2023-02-15T14:10:42Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
  Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
  arXiv  Detail & Related papers  (2022-04-27T13:10:48Z)
• A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data [0.17205106391379021]
  This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene.
  arXiv  Detail & Related papers  (2021-08-27T16:16:01Z)
• Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information [2.984037222955095]
  This article presents a novel approach to incorporate visual cues from video-data from a wide-angle stereo camera system mounted at an urban intersection into the forecast of cyclist trajectories. We extract features from image and optical flow sequences using 3D convolutional neural networks (3D-ConvNet) and combine them with features extracted from the cyclist's past trajectory to forecast future cyclist positions.
  arXiv  Detail & Related papers  (2021-06-30T11:34:43Z)
• End-to-End Deep Structured Models for Drawing Crosswalks [98.9901717499058]
  We project both inputs onto the ground surface to produce a top down view of the scene. We then leverage convolutional neural networks to extract semantic cues about the location of the crosswalks. Experiments over crosswalks in a large city area show that 96.6% automation can be achieved.
  arXiv  Detail & Related papers  (2020-12-21T18:59:08Z)
• SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation [4.350338899049983]
  We propose a generalization of PointPainting to be able to apply fusion at different levels. We show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird's eye view pedestrian detection benchmarks.
  arXiv  Detail & Related papers  (2020-09-25T14:52:32Z)
• Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation [11.174536284571802]
  We propose a supervised trajectory segmentation algorithm called Wise Sliding Window (WS-II) WS-II processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal. We evaluate our method over three real datasets from different domains.
  arXiv  Detail & Related papers  (2020-03-23T12:55:40Z)
• From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds [59.98665358527686]
  We propose a new method for segmentation-free joint estimation of orthogonal planes. Such unified scene exploration allows for multitudes of applications such as semantic plane detection or local and global scan alignment. Our experiments demonstrate the validity of our approach in numerous scenarios from wall detection to 6D tracking.
  arXiv  Detail & Related papers  (2020-01-21T06:51:47Z)

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.