FADE: A Dataset for Detecting Falling Objects around Buildings in Video

• URL: http://arxiv.org/abs/2408.05750v1
• Date: Sun, 11 Aug 2024 11:43:56 GMT
• Title: FADE: A Dataset for Detecting Falling Objects around Buildings in Video
• Authors: Zhigang Tu, Zitao Gao, Zhengbo Zhang, Chunluan Zhou, Junsong Yuan, Bo Du,
• Abstract summary: Falling objects from buildings can cause severe injuries to pedestrians due to the great impact force they exert. FADE contains 1,881 videos from 18 scenes, featuring 8 falling object categories, 4 weather conditions, and 4 video resolutions. We develop a new object detection method called FADE-Net, which effectively leverages motion information.
• Score: 75.48118923174712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Falling objects from buildings can cause severe injuries to pedestrians due to the great impact force they exert. Although surveillance cameras are installed around some buildings, it is challenging for humans to capture such events in surveillance videos due to the small size and fast motion of falling objects, as well as the complex background. Therefore, it is necessary to develop methods to automatically detect falling objects around buildings in surveillance videos. To facilitate the investigation of falling object detection, we propose a large, diverse video dataset called FADE (FAlling Object DEtection around Buildings) for the first time. FADE contains 1,881 videos from 18 scenes, featuring 8 falling object categories, 4 weather conditions, and 4 video resolutions. Additionally, we develop a new object detection method called FADE-Net, which effectively leverages motion information and produces small-sized but high-quality proposals for detecting falling objects around buildings. Importantly, our method is extensively evaluated and analyzed by comparing it with the previous approaches used for generic object detection, video object detection, and moving object detection on the FADE dataset. Experimental results show that the proposed FADE-Net significantly outperforms other methods, providing an effective baseline for future research. The dataset and code are publicly available at https://fadedataset.github.io/FADE.github.io/.

Related papers

• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• Retrieval Robust to Object Motion Blur [54.34823913494456]
  We propose a method for object retrieval in images that are affected by motion blur. We present the first large-scale datasets for blurred object retrieval. Our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets.
  arXiv  Detail & Related papers  (2024-04-27T23:22:39Z)
• Recent Trends in 2D Object Detection and Applications in Video Event Recognition [0.76146285961466]
  We discuss the pioneering works in object detection, followed by the recent breakthroughs that employ deep learning. We highlight recent datasets for 2D object detection both in images and videos, and present a comparative performance summary of various state-of-the-art object detection techniques.
  arXiv  Detail & Related papers  (2022-02-07T14:15:11Z)
• Learning to Track Object Position through Occlusion [32.458623495840904]
  Occlusion is one of the most significant challenges encountered by object detectors and trackers. We propose a tracking-by-detection approach that builds upon the success of region based video object detectors. Our approach achieves superior results on a dataset of furniture assembly videos collected from the internet.
  arXiv  Detail & Related papers  (2021-06-20T22:29:46Z)
• Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme [70.45901040613015]
  We study the new problem of few-shot learning for video object detection. We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
  arXiv  Detail & Related papers  (2021-03-26T20:37:55Z)
• Ensembling object detectors for image and video data analysis [98.26061123111647]
  We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
  arXiv  Detail & Related papers  (2021-02-09T12:38:16Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Plug & Play Convolutional Regression Tracker for Video Object Detection [37.47222104272429]
  Video object detection targets to simultaneously localize the bounding boxes of the objects and identify their classes in a given video. One challenge for video object detection is to consistently detect all objects across the whole video. We propose a Plug & Play scale-adaptive convolutional regression tracker for the video object detection task.
  arXiv  Detail & Related papers  (2020-03-02T15:57:55Z)

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.