Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading

• URL: http://arxiv.org/abs/2301.08406v1
• Date: Fri, 20 Jan 2023 02:51:53 GMT
• Title: Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading
• Authors: Hao Wang and Hao Bao and Liekang Zeng and Ke Luo and Xu Chen
• Abstract summary: Hode is an offloaded analytic framework that utilizes multiple edge nodes in proximity to expedite pedestrian detection with high-resolution inputs. Hode can achieve up to 2.01% speedup with very mild accuracy loss.
• Score: 13.672372305669116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To identify dense and small-size pedestrians in surveillance systems, high-resolution cameras are widely deployed, where high-resolution images are captured and delivered to off-the-shelf pedestrian detection models. However, given the highly computation-intensive workload brought by the high resolution, the resource-constrained cameras fail to afford accurate inference in real time. To address that, we propose Hode, an offloaded video analytic framework that utilizes multiple edge nodes in proximity to expedite pedestrian detection with high-resolution inputs. Specifically, Hode can intelligently split high-resolution images into respective regions and then offload them to distributed edge nodes to perform pedestrian detection in parallel. A spatio-temporal flow filtering method is designed to enable context-aware region partitioning, as well as a DRL-based scheduling algorithm to allow accuracy-aware load balance among heterogeneous edge nodes. Extensive evaluation results using realistic prototypes show that Hode can achieve up to 2.01% speedup with very mild accuracy loss.

Related papers

• High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity [69.32473738284374]
  We propose DiffDIS, a diffusion-driven segmentation model that taps into the potential of the pre-trained U-Net within diffusion models. By leveraging the robust generalization capabilities and rich, versatile image representation prior to the SD models, we significantly reduce the inference time while preserving high-fidelity, detailed generation. Experiments on the DIS5K dataset demonstrate the superiority of DiffDIS, achieving state-of-the-art results through a streamlined inference process.
  arXiv  Detail & Related papers  (2024-10-14T02:49:23Z)
• EDCSSM: Edge Detection with Convolutional State Space Model [3.649463841174485]
  Edge detection in images is the foundation of many complex tasks in computer graphics. Due to the feature loss caused by multi-layer convolution and pooling architectures, learning-based edge detection models often produce thick edges. This paper presents an edge detection algorithm which effectively addresses the aforementioned issues.
  arXiv  Detail & Related papers  (2024-09-03T05:13:25Z)
• RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model [3.580983453285039]
  We present a novel detection algorithm which can maintain high sub-pixel precision on inputs under multiple interferences. The whole algorithm, adopting a coarse-to-fine strategy, contains a X-corner detection network and three post-processing techniques. Evaluations on real and synthetic images indicate that the presented algorithm has the higher detection rate, sub-pixel accuracy and robustness than other commonly used methods.
  arXiv  Detail & Related papers  (2023-07-07T10:40:41Z)
• 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)
• DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions [30.892930944644853]
  We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios.
  arXiv  Detail & Related papers  (2022-02-05T13:46:47Z)
• Gated2Gated: Self-Supervised Depth Estimation from Gated Images [22.415893281441928]
  Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth. We propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal.
  arXiv  Detail & Related papers  (2021-12-04T19:47:38Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes [9.434133337939496]
  We consider a scenario in which an autonomous vehicle operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. We model the sensing field using a multi-fidelity Gaussian process that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm called Multi-Target Search (EMTS) that addresses the coverage-accuracy trade-off.
  arXiv  Detail & Related papers  (2020-05-18T02:53:52Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

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.