This document summarizes a presentation on object classification in far field and low resolution videos. It defines object classification and classifiers, describes supervised and unsupervised classification approaches, outlines steps for video-based classification including image-based and background subtraction methods, explains why classification in far field videos is challenging, and concludes by seeking collaboration from others working on related topics.

1. OBJECT CLASSIFICATION
IN FAR FIELD AND LOW
RESOLUTION VIDEOS
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Proposed by : Pr. S. Larabi
Presented by : Setitra Insaf
Degree : 2nd year doctoral student
Universiy : USTHB Algeria
Email : setita.insaf@hotmail.com

2. PLAN
 What is object classification
 Object classification can be either supervsed or
unsupervised
 What is an object classfier
 Defintion
 Conditions
 types
 What are steps of object classification in videos
 Two approches : image based and background
substraction
 Why is this work particular
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 Conclusion

3. WHAT IS OBJECT CLASSIFICATION?
OBJECT CLASSIFICATION CAN BE EITHER SUPERVISED OR UNSUPERVISED
Class label= car Class label= person Class label= bicyle …
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4. WHAT IS OBJECT CLASSIFICATION?
OBJECT CLASSIFICATION CAN BE EITHER SUPERVISED OR UNSUPERVISED
 Supervised classification= there is a supervisor for
classifying:
 ~machine learning: machine learns from exemples
 First select exemples= near field/far field, indoor/outdoor, =/=
scales and translation ..etc.
 Label all exemples
 Train the classifier with these exemples
 Apply the classification algorithm on new images
 Unsupervised learning: no supervisor for classifying
 ~Segmentation Consider only supervised 4
classification for this
presentatiion

5. WHAT IS AN OBJECT CLASSIFIER?
 Definition
 Algorithm of classification
 Conditions
 For robust classification:
 Works in different conditions (weather, resolution, compression
…etc)
 Answer to a multiclass problem
 Works on realtime constrains
 Types
 SVM
 NN
 Naive Baysien
 … 5

6. What are steps of object classification in videos ?
Two approches : image based and background substraction
 Image based: classification of every image alone
and make a decision based on labels of every
image
 Background Substraction(BS): use movment as a
useful information for detecting regions of interest
(moving objets) and then classify blobs resulting on
BS
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7. What are steps of object classification in videos ?
Two approches : image based and background substraction
 BS classificication
 Image based
 Background
classification substraction (mixture of
 Feature gaussian, optical
flow..etc)=>binary
extraction/selection images with blobs
 Object classification  Exemple of BS:
(SVM, NN, Bayes  Object tracking
classifier…)  Image improvement
(removing supurious
objects : shadows,
false moving objects..)
 Feature
extraction/selection
(SIFT, Haar, HOG,
DHOG ..)
 Object classification 7

8. Why is this work particular?
 Because pattern recognition is a wide area of
research
 Because classifying in far field videos is a big
challenge
 Small objects
 Occlusion
 Dificulties of application of existing algorithms (part based
classifiers for exemple)
 Insatisfying results as far
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9. Conclusion
 Presentation of principal ideas of the work
 Seeking for people working on:
 Pattern recognition
 Clasification algorithms: SVM, NN, Bayes… (definitly, mathematical
and probabilistic formulas are hard to understand!!!!)
 Feature extraction and selection
 Image and video retrieval
 …
Thanks for attention dear collegues!!
Questions? Feel free to ask !
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