Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition

• URL: http://arxiv.org/abs/2006.15873v1
• Date: Mon, 29 Jun 2020 08:50:20 GMT
• Title: Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition
• Authors: Chunhua Jia, Wenhai Yi, Yu Wu, Hui Huang, Lei Zhang, Leilei Wu
• Abstract summary: We present a work-flow which aims at capturing residents' abnormal activities through the passenger flow of elevator in multi-storey residence buildings. Camera and sensors (hall sensor, photoelectric sensor, gyro, accelerometer, barometer, and thermometer) with internet connection are mounted in elevator to collect image and data. Computer vision algorithms such as instance segmentation, multi-label recognition, embedding and clustering are applied to generalize passenger flow of elevator.
• Score: 18.166284261575473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a work-flow which aims at capturing residents' abnormal activities through the passenger flow of elevator in multi-storey residence buildings. Camera and sensors (hall sensor, photoelectric sensor, gyro, accelerometer, barometer, and thermometer) with internet connection are mounted in elevator to collect image and data. Computer vision algorithms such as instance segmentation, multi-label recognition, embedding and clustering are applied to generalize passenger flow of elevator, i.e. how many people and what kinds of people get in and out of the elevator on each floor. More specifically in our implementation we propose GraftNet, a solution for fine-grained multi-label recognition task, to recognize human attributes, e.g. gender, age, appearance, and occupation. Then anomaly detection of unsupervised learning is hierarchically applied on the passenger flow data to capture abnormal or even illegal activities of the residents which probably bring safety hazard, e.g. drug dealing, pyramid sale gathering, prostitution, and over crowded residence. Experiment shows effects are there, and the captured records will be directly reported to our customer(property managers) for further confirmation.

Related papers

• Raising the Bar(ometer): Identifying a User's Stair and Lift Usage Through Wearable Sensor Data Analysis [4.453838343573515]
  This research describes a new exploratory dataset, to examine the patterns and behaviors related to using stairs and lifts. We collected data from 20 participants while climbing and descending stairs and taking a lift in a variety of scenarios. Our method is highly accurate at classifying stair and lift operations with an accuracy of 87.61% and a multi-class weighted F1-score of 87.56% over 8-second time windows.
  arXiv  Detail & Related papers  (2024-09-18T06:26:50Z)
• Elevator, Escalator or Neither? Classifying Pedestrian Conveyor State Using Inertial Navigation System [12.796931853596902]
  Knowing a pedestrian's conveyor state of "elevator," "escalator," or "neither" is fundamental in many applications such as indoor navigation and people flow management. We study, for the first time, classifying the conveyor state of a pedestrian, given the multimodal INS (inertial navigation system) readings of accelerometer, gyroscope and magnetometer sampled from the pedestrian phone. We propose ELESON, a novel, effective and lightweight INS-based deep learning approach to classify whether a pedestrian is in an elevator, escalator or neither.
  arXiv  Detail & Related papers  (2024-05-06T07:27:30Z)
• Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
  This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity. The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies. They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
  arXiv  Detail & Related papers  (2024-02-14T13:20:24Z)
• Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation [76.45506517198956]
  Object detection in low-light scenarios has attracted much attention in the past few years. A mainstream and representative scheme introduces enhancers as the pre-processing for regular detectors. In this work, we try to arouse the potential of enhancer + detector.
  arXiv  Detail & Related papers  (2023-09-07T08:11:47Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• Self-Supervised Transformers for Activity Classification using Ambient Sensors [3.1829446824051195]
  This paper proposes a methodology to classify the activities of a resident within an ambient sensor based environment. We also propose a methodology to pre-train Transformers in a self-supervised manner, as a hybrid autoencoder-classifier model.
  arXiv  Detail & Related papers  (2020-11-22T20:46:25Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints [0.7424262881242935]
  We introduce a novel tracking-by-detection framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios. Our approach improves object detection by employing a test-time data augmentation procedure. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2020-07-15T18:09:31Z)
• Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos [72.50607929306058]
  We propose a real-time online system to perform activity detection on untrimmed security videos. The proposed method consists of three stages: tubelet extraction, activity classification and online tubelet merging. We demonstrate the effectiveness of the proposed approach in terms of speed (100 fps) and performance with state-of-the-art results.
  arXiv  Detail & Related papers  (2020-04-23T22:20:10Z)
• Contextual Sense Making by Fusing Scene Classification, Detections, and Events in Full Motion Video [0.7348448478819135]
  We aim to address the needs of human analysts to consume and exploit data given aerial FMV. We have divided the problem into three tasks: (1) Context awareness, (2) object cataloging, and (3) event detection. We have applied our methods on data from different sensors at different resolutions in a variety of geographical areas.
  arXiv  Detail & Related papers  (2020-01-16T18:26:34Z)

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.