Training Data Extraction and Object Detection in Surveillance Scenario

Artur Wilkowski , Maciej Stefańczyk , Włodzimierz Kasprzak

Abstract

Police and various security services use video analysis for securing public space, mass events, and when investigating criminal activity. Due to a huge amount of data supplied to surveillance systems, some automatic data processing is a necessity. In one typical scenario, an operator marks an object in an image frame and searches for all occurrences of the object in other frames or even image sequences. This problem is hard in general. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability, and learning from small data sets. In the system proposed here, we use a two-stage detector. The first region proposal stage is based on a Cascade Classifier while the second classification stage is based either on a Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The proposed configuration ensures both speed and detection reliability. In addition to this, an object tracking and background-foreground separation algorithm is used, supported by the GrabCut algorithm and a sample synthesis procedure, in order to collect rich training data for the detector. Experiments show that the system is effective, useful, and applicable to practical surveillance tasks.
Author Artur Wilkowski (FEIT / AK)
Artur Wilkowski,,
- The Institute of Control and Computation Engineering
, Maciej Stefańczyk (FEIT / AK)
Maciej Stefańczyk,,
- The Institute of Control and Computation Engineering
, Włodzimierz Kasprzak (FEIT / AK)
Włodzimierz Kasprzak,,
- The Institute of Control and Computation Engineering
Journal seriesSensors, [SENSORS-BASEL], ISSN 1424-8220, e-ISSN 1424-3210
Issue year2020
Vol20
No9
Pages1-25
Publication size in sheets134.45
Article number2689
Keywords in Englishobject detection; few shot learning; SVM; CNN; cascade classifier; video surveillance
ASJC Classification1303 Biochemistry; 1602 Analytical Chemistry; 2208 Electrical and Electronic Engineering; 3107 Atomic and Molecular Physics, and Optics
DOIDOI:10.3390/s20092689
URL https://www.mdpi.com/1424-8220/20/9/2689
ProjectNational Cybersecurity Platform. Project leader: Niewiadomska-Szynkiewicz Ewa, , Phone: 3650, start date 01-09-2017, planned end date 31-08-2020, IA/NPC/2017, Implemented
WEiTI Projects financed by NCRD [Projekty finansowane przez NCBiR (NCBR)]
Languageen angielski
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Wilkowski i in sensors-20-02689-v2.pdf 1.55 MB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 12-07-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.393; WoS Impact Factor: 2018 = 3.031 (2) - 2018=3.302 (5)
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