The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.

1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1389
Survey on Detection of Crime
Sandhya Karkhele
Student, Department of Sinhgad Academy of Engineering, Savitribai Phule Pune, University, Pune, Maharashtra
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - While the action recognition community has been completely focused on finding simple actions such as approval,
walking or exercise, the detection of non-aggressive fighting or behavior has been relatively less studied. This ability is also very
useful in some cases of police video surveys, such as in prisons, medical specialties, senior centers or even camera phones. Once we
have analyzed the analysis of the previous approaches, we have the inclination to take a look at the familiarWordBagframeused
for the recognition of the action within the precise disadvantage of the fight detection, along with two of the action descriptors i
most important currently available: STIP and MoSIFT. In order to study and encourage theanalysisofvideoviolencedetection, we
have a tendency to present a replacement video that contains cardinal sequences divided into two groups: fights and non-fights.
Experiments with this information and another with action film fights show that the fights are detected with a precision of about
ninety.
Key Words: Vision Systems, Fire Detection, Smart Cameras, Computer Vision, Object Detection
1. INTRODUCTION
Surveillance cameras play a major role in providing reliable security to humanity. The need for an automatic real-time
surveillance system that can help control the occurrence of certain dangerous events is very much in demand. Many unusual
events include terrorist attacks, unusual behavior; The placement of bombs, traffic problems, attacks on ATMs, etc. require
immediate attention to avoid damage to the company. The real-time detection of these anomalous events is a key problem,
since it always requires the attention of security personnel without leaving roomforhumanerrors.Thedetectionof anomalies
is also known as detection of novelties, anomalies, anomalies,anomalies, rare eventsor detectionofanomalousvalues,which is
a general and diversified topic in the field of artificial vision research that can be applied to many other fields, such as
marketing, medical diagnosis, network intrusion. etc., not limited to visual surveillance. The problem of anomaly detection is
highly specific for applications and is diversified in terms of approaches, data sets, problem interpretation, hypotheses,
definition and objectives.
2. LITERATURE SURVEY
Ismael Serrano, Oscar Deniz, Jose L [1],outlined the Fight acknowledgment in video using Hough Forests and 2D
Convolutional Neural Network. One of the first proposals for violence recognition in video. One of the first proposals for
violence recognition in video. Investigational results have been obtained for recognition of actions such as walking, jogging,
pointing or hand waving [4]. However, action detection has been devoted comparativelylesseffort.Violencedetectionisa task
that can be leveraged in real-life applications. While there is a large number of studied datasets for action recognition,specific
datasets with a relevant number of violent sequences (fights) were not available until, where the authors created two specific
datasets for the fight/violence problem testing state-of-the-art methods on them. The features are compact: Finding 53.2%
accuracy on UCF-101 dataset with only ten dimensions and also very efficient to computedueto thefastinferenceofConvents.
Finally, they are very simple and easy to train and uses
Daniel Bernhardt[3] outlined Detecting emotionsfrom everydaybodymovements. Thehuman bodyisa complexhierarchical
structure which has evolved to enable us to perform sophisticated tasks. At the same time, movements and posture of our
limbs, head and torso communicate affect and inter-personal attitudes. To a large extent our functioning as sociallyintelligent
individuals relies on our ability to decode the affective and expressive cues we perceive through facial or body gestures.
Research suggests that our responses to avatars in Immersive Virtual Environments (IVEs) are governed by our expectations
about the presence and correct exhibition ofthoseexpressivecues.Scenarios.First,bodyexpressionscouldconveyancefurther
social cue to strengthen or complement facial expressions. Second, thisapproachcouldalsobemostpopularoncetheuserisn't
facing the detector at associate degree adequate distance required to effectively method countenance.
GinevraCastellano [5] outlined the Recognising Human Emotions from Body Movement and Gesture Dynamics. One critical
aspect of human-computer interfaces is the ability to communicate with users in an expressiveway.Computersshouldbeable
to recognise and interpret users’ motional states and to communicate expressive-emotional information to them. Recently,
there has been an increased interest in designing automated video analysis algorithms aiming to extract, describe andclassify
information related to the emotional state of individuals. In this paper we focus on video analysis of movement and gestureas

2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1390
indicators of an underlying emotional process. Our research aims to investigate which are the motion cues indicating
differences between emotions and to define a model to recognise emotions fromvideo analysisofbodymovement andgesture
dynamics. Recognizing Human Emotions from Body Movement and Gesture Dynamics and how to construct a pervasive
recognition system based on” real-life” emotion data is still a challenging open question.
Domenico D. Bloisi [6]A paper proposed on “Online real-time crowd behavior detection in video sequences” state an
algorithm called FSCB. FSCB is made of three main steps: (1) Feature detection and temporal filtering;(2)imageSegmentation
and blob extraction; (3) Crowd Behaviour detection. In this paper, a real-time andonlinecrowd behaviourdetectionalgorithm
for video sequences is described. The algorithm, called FSCB, is based on a pipeline made of the following stages: (1) stable
features are tracked between frames of the sequence; (2) a temporal mask is extracted; (3) moving blobs are found using
segmentation; (4) anomalous events are detected using two measures, i.e., instant entropy and temporal occupancyvariation.
Quantitative experiments have been conducted on different publicly available data sets: UMN, PETS2009,andAGORASET.For
PETS 2009 and AGORASET, ground truth data have been produced and made available at the FSCB website. Furthermore, a
novel annotated data set, containing crowded scenes from the start of a marathon, has been created. FSCB has been
quantitatively compared with other stateof-the art methods for online crowd event detection. The results of the comparison
demonstrate the effectiveness of the proposed approach, that works without the need of a training stage and obtain real-time
performance on 320×240 images. In this paper obtain real-time performance result on 320×240 image
H. Yeh, C. Y. Lin, K. Muchtar, H. E. Lai and M. T. Sun [7]A paper proposed on “Three-Pronged Compensation and Hysteresis
Thresholding for Moving Object Detection in Real-Time Video Surveillance” proposed moving object detection method. This
method is a three-pronged approach to compensate in order to extract foreground objects as complete as possible.First,usea
texture background modelling method, which only detects the texture of the foreground object but can resist illumination
changes and shadow interference. Second, apply hysteresis thresholding on both texture and colour background models to
generate predominant and supplementary images. The combination of predominant images shows the skeleton of moving
objects, PCT. Then use several supplementary images to mend the shape of PCT with the goal of completing moving object
extraction without shadows. Finally, the proposed motion history applies spatial-temporal information to alleviate the cavity
and fragment problems in foreground objects. The combined approach thereby offers a three-pronged compensation by
leveraging texture, color, and spatial-temporal information. In this paper different cases especially under scenarios in which
objects move toward the camera or move slowly can achieve 47 fps with 1080p on FPGA implementation performance of the
proposed method is suitable for real-time surveillance systems.
S. Coşar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares and F. Brémond, [8] A paper proposed on “Towards Abnormal
Trajectory and Event Detection in Video Surveillance” focused on trajectory-based and pixel based approaches for
unsupervised abnormal behavior detection. A) Object and Group Tracking: As the first step of this approach, it take the input
video and extract all trajectories in the scene. In this step, it run the object tracking algorithm and group tracking algorithm to
generate all individual trajectories of objects/groups moving inthescene.B)Grid-BasedAnalysis:Thissteptakestheextracted
trajectories and bounding boxes of each objectasinputandperformsgrid-basedanalysis.Inthegrid-basedanalysis,threemain
steps are performed: trajectory snapping, zone discovery, and trajectory-based anomaly detection. Tested our approachwith
three datasets that include different types of abnormality. Experimental results show that our approach is able to detect all
kinds of abnormal events, including the wrong direction, loitering, stopping, nopaymentinmetro/subwayvideos,fightingand
car moving in parking lot videos. Although there are some missed events, our approach is able to detect all types of abnormal
events with a very low number of false alarms when compared to existing approaches.
Irjet Templatesample paragraph .Define abbreviationsandacronymsthefirsttimetheyareusedinthetext,evenafterthey have
been defined in the abstract. Abbreviations such as IEEE, SI, MKS, CGS, sc, dc, and rms do not have to be defined. Do not use
abbreviations in the title or heads unless they are unavoidable.
Recognizing fights associated aggressive behavior in video is associate a lot of and a lot of necessary application area. Such
capability might even be terribly useful in video investigating things like in prisons, medication or senior centers. Action
recognition tech-niques that have targeted for the foremosthalfonindividual actorsandsimple eventssquaremeasureusually
extended to the present specific application.Thispaper evaluatesbutprogressivevideodescriptorscanperformfightdetection
on two new datasets: a 1000-video assortment of NHL hockey games and a smaller 200-clip assortment of scenes from
3. CONCLUSION

3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1391
REFERENCES
[1] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. Learning spatiotemporal featuresthrough3dconvolutional
networks. In 2015 IEEE worldwide Conference on Computer Vision (ICCV), pages 4489–4497. IEEE, 2015.
[2] DanielBernhardt,“Detecting emotions from everyday body movements) University of Cambridge.
[3] Justin Lai, “Developing a Real-Time Gun Detection Classifier”, World academy of science, Stanford University,
[4] Ginevra Castellano1, Santiago D. Villalba2, “Recognising Human Emotions from Body Movement and Gesture
Dynamics”, University of Genoa
[5] AndreaPennisi, Domenico D. Bloisi, Luca Iocchi, Online real-time crowd behavior detection in video sequences, In
Computer Vision and Image Understanding, Volume 144, 2016.
[6] C. H. Yeh, C. Y. Lin, K. Muchtar, H. E. Lai and M. T. Sun, "Three-Pronged Compensation and Hysteresis Thresholdingfor
Moving Object Detection in Real-Time Video Surveillance," in IEEE Transactions on Industrial Electronics, vol.64,no.
6, pp. 4945-4955, June 2017.
[7] S.Coşar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares and F. Brémond, "Toward Abnormal route and Event
Detection in Video Surveillance," in IEEE Transactions on circuit and system used for Video Technology, vol.27,no. 3,
pp. 683-695, March 2017
[8] MoezBaccouche, et al. Sequential deep learning for human action recognition. International Workshop on Human
BehaviorUnderstanding.Springer Berlin Heidelberg, 2011.
[9] TobiasSenst, Volker Eiselein, “A Local Feature based on Lagrangian Measures for Violent Video Classification(IEEE),
TechnischeUniversitat Berlin, Germany
[10] Samir K. Bandyopadhyay, BiswajitaDatta, and Sudipta Roy Identifications of concealed weapon in a Human
Body Department of Computer Science and Engineer, University of Calcutta, 2012.
BIOGRAPHIES
Name: Sandhya Ajinath Karkhele
Branch :Computer EngineeringME