Time Series Analysis of Garment Distributions Via Street Webcam

Sen Jia, Tom Lansdall-Welfare, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
309 Downloads (Pure)


The discovery of patterns and events in the physical world by analysis of multiple streams of sensor data can provide benefit to society in more than just surveillance applications by focusing on automated means for social scientists, anthropologists and marketing experts to detect macroscopic trends and changes in the general population. This goal complements analogous efforts in documenting trends in the digital world, such as those in social media monitoring. In this paper we show how the contents of a street webcam, processed with state-of-the-art deep networks, can provide information about patterns in clothing and their relation to weather information. In particular, we analyze a large time series of street webcam images, using a deep network trained for garment detection, and demonstrate how the garment distribution over time significantly correlates to weather and temporal patterns. Finally, we additionally provide a new and improved labelled dataset of garments for training and benchmarking purposes, reporting 58.19% overall accuracy on the ACS test set, the best performance yet obtained.
Original languageEnglish
Title of host publicationImage Analysis and Recognition
Subtitle of host publication13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings
EditorsAurélio Campilho, Fakhri Karray
Number of pages8
ISBN (Electronic)9783319415017
ISBN (Print)9783319415000
Publication statusPublished - 25 Jul 2016
EventInternational Conference on Image Analysis and Recognition - Povoa de Varzim, Portugal
Duration: 13 Jul 201615 Jul 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceInternational Conference on Image Analysis and Recognition
CityPovoa de Varzim


  • Information Fusion
  • Garment Classification
  • Deep Learning

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