Transcript Abandoned object detection in traffic surveillance videosx

Abandoned object detection in
traffic surveillance videos
Group 18b: Rahul Sankhwar
CS365A: Project Presentation
1
Motivation
Due to increase in terrorism there is a need for a
good surveillance system. Detection of
abandoned objects is an integral part of it.
D Pathak, A Sharang, A Mukerjee, “Anomaly
Localization in Topic-based Analysis of
Surveillance Videos” IEEE Winter Conference on
Applications of Computer Vision (WACV 2015).
Modelling
• Unsupervised
Modelling
Detection
• Anomalous Clip
Detection
Localization
• Spatio-Temporal
Anomaly Localization
Modeling
The authors modeled the anomaly detection
problem analogous to topic modeling in NLP.
Reference: BTP report. D Pathak and A
Sharang
Visual word formation
Video
Frame
Vibe Foreground Extractor
Foreground Image
Location
HOG-HOF
descriptor
3 dimensions of visual
word
Blob Size
Reference: BTP report. D Pathak and A
Sharang
• Location :
• Each frame of dimension m x n is divided into blocks of 20 x 20
• HOG - HOF descriptor :
• For each block, a foreground pixel was selected at random and spatiotemporal descriptor was computed around it.
• From the descriptors obtained from the training set, 200,000 descriptors
were randomly selected. 20 cluster centres were obtained from these
descriptors by k-means clustering.
• Each descriptor was assigned to one of these centres.
• Size :
• In each block , we compute the connected components of the foreground
pixels
• The size of the connected components is quantised to two values: large and
small
Reference: BTP report. D Pathak and A
Sharang
Parametric Bayesian Modeling
Video Clip
Visual Words Extraction
Parametric Bayesian Model (pLSA)
pLSA
pLSA model gives us likelihood of documents in
the topic space, i.e. given a document it gives
the probability that the document belongs to a
certain topic.
Topic vector
Given a document, from pLSA, we can get the
probability that the document belongs to a
certain topic.
If there are K topics we can get a K dimensional
vector where i-th dimension tells us the
likelihood corresponding to i-th topic.
This is called the topic vector.
Detection
The authors proposed an efficient Projection
model algorithm for detection of anomaly.
Reference: BTP report. D Pathak and A
Sharang
Preliminaries
• Bhattacharyya Distance :
•
If the documents 𝑑𝑥 and 𝑑𝑦 are represented by the probability distributions in topic
space as 𝜃 𝑥 and 𝜃 𝑦 respectively, then distance is defined by d = − log
𝑦
𝑖
𝜃𝑖𝑥 𝜃𝑖
• Cumulative histogram of m documents:
•
A histogram obtained by stacking the word count histogram of the m documents.
• Spatial neighbourhood of a word :
•
For a word at location 𝑖, 𝑗 , all words at locations 𝑖 ± 1, 𝑗 ± 1 , 𝑖 ± 1, 𝑗 and 𝑖, 𝑗 ± 1
are the spatial neighbours of the word.
• Significant distribution of neighbourhood word :
•
The distribution of a word is significant if its frequency in the cumulative histogram
is greater than a threshold 𝑡ℎ𝑛𝑏𝑟
Reference: BTP report. D Pathak and A
Sharang
Localization
• Spatial Localization :
Every word has location information in it. Therefore we can directly
localize the anomalous words in test document to their spatial
locality.
• Temporal Localization :
If we maintain a list of frame numbers corresponding to documentword pair, we can tag the frames with anomalous words.
Reference: BTP report. D Pathak and A
Sharang
Relation to detection of abandoned
objects
The previous paper does not detect abandoned objects,
since in the visual word formation abandoned objects are
not being able to classified in features of the visual word.
Reason:
Foreground extraction mechanism used in the paper,
Vibe, is based on motion cues and models abandoned
objects/vehicles as foreground for few frames but then
this information dies out. Therefore, problem with
abandoned objects is that they loose the foreground
characteristic after sometime.
Solution
Instead of using ViBe, we can use different foreground
extraction mechanism.
The following paper efficiently captures abandoned
objects in the foreground efficiently:
Y.L. Tian, R.S. Feris, H. Liu, A. Hampapur and M.T. Sun,
“Robust detection of abandoned and removed objects
in complex surveillance videos,” IEEE Transactions on
Systems, Man and Cybernatics-PartC:Applications and
Reviews, vol.41, no.5, pp. 565-576, 2011.
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5
571035&tag=1
Work done so far..
Ran code and done analysis for, “Anomaly
Localization in Topic-based Analysis of
Surveillance Videos”, on the traffic highway
dataset.
Code is available to us by the authors:
http://www.cse.iitk.ac.in/users/abhisg/btp/abhi
sg.zip
Dataset:
http://www.eecs.qmul.ac.uk/˜andrea/avss2007
_d.html
Future work
In the algorithm proposed in anomaly detection
paper, instead of using ViBe, employing different
foreground extraction mechanism.
Using cosine distance instead of Bhattacharyya
Distance.
References
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•
•
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D Pathak, A Sharang, A Mukerjee, “Anomaly Localization in Topic-based Analysis of Surveillance Videos”
IEEE Winter Conference on Applications of Computer Vision (WACV 2015).
O. Barnich and M. Van Droogenbroeck. “Vibe: A universal background subtraction algorithm for video
sequences.” Image Processing, IEEE Transactions on, 20(6):1709–1724, 2011.
I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1–8. IEEE, 2008.
Hofmann, Thomas. "Probabilistic latent semantic indexing." Proceedings of the 22nd annual international
ACM SIGIR conference on Research and development in information retrieval. ACM, 1999
Mahadevan, Vijay, et al. "Anomaly detection in crowded scenes." Computer Vision and Pattern
Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010.
BTP Report and presentation on, “Unsupervised Modeling, Detection and Localization of Anomalies in
Surveillance Videos”
D Pathak, A Sharang, A Mukerjee.
Thank you
pLSA : Topic Model
• Fixed number of topics :
𝑧1 , 𝑧2 … 𝑧𝑘 . Each word in the
vocabulary is attached with a single
topic.
• Topics are hidden variables. Used
for modelling the probability
distribution
• Computation
– Marginalise over hidden variables
– Conditional independence
assumption: p(w|z) and p(d|z) are
independent of each other
Reference: BTP report. D Pathak and A
Sharang
EM Algorithm: Intuition
• E-Step
– Expectation step where expectation of the
likelihood function is calculated with the current
parameter values
• M-Step
– Update the parameters with the calculated
posterior probabilities
– Find the parameters that maximizes the likelihood
function
Reference: BTP report. D Pathak and A
Sharang
EM: Formalism
Reference: BTP report. D Pathak and A
Sharang
EM in pLSA: E Step
• It is the probability that a word w occurring in
a document d, is explained by aspect z
(based on some calculations)
Reference: BTP report. D Pathak and A
Sharang
EM in pLSA: M Step
• All these equations use p(z|d,w) calculated in E
Step
• Converges to local maximum of the likelihood
function
Reference: BTP report. D Pathak and A
Sharang