Motion Trajectory Based Video Retrieval


Introduction

 

Original Video Query

It's a sample video taken from UCF Sports Action dataset. The content of the video is difficult to

express in words and will vary from person to person. On the right-hand side a sample online

user sketch which can be used to retrieve the video has been shown

Content based video retrieval is an active area in Computer Vision. The most common type of retrieval strategies we know about are query by text (Youtube, Vimeo etc) and query by example-video or image (Video Google). When the query is in the form of a text, most of the current systems search the tags and metadata associated with the video. A problem with such an approach is that the tags or metadata need not be the real content of the video and are misleading. Moreover, often the queries may be abstract and lenghty. For example, " a particular diving style in swimming where the swimmer does three somersaults before diving " The other method i.e the query by example paradigm is limited by the absence of an example in hand at the right time.

Instead, certain videos can be identified from unique motion trajectories. For example the query mentioned above or another query like "the first strike in carrom where three or more carrom men or disks go to pockets" or "All red cars which came straight from North and then took a left turn". Clearly, queries like these describe the actual content of the video, which is unlikely to be found from the tags and metadata. Under these circumstances, queries can be framed using sketches. A basic sketch containing the object and the motion patterns of the object should suffice to describe the actual content of the video. Sketches can be offline (images) or online(temporal data collected using a tablet).

In this project we are trying to build a sketch-based video retrieval system using online sketches as queries.


Challenges

Although sketch-based search appears to be a very intuitive way to depict the content of a video, it suffers from perceptual variability. In simple words multiple users perceive the same motion in different ways. The variability is in terms of spatio-temporal properties like shape, direction, scale and speed. The following figures illustrate the problem precisly.

Original Video User: 1 User: 2 User: 3  

A sample sinusoidal motion and its three different user interpretations

If we try to match these different representations in 2D Euclidean space then they wont match. Beacause quantitatively they are different. But they have qualitative similarity. So we have to project these sketches to a space where they are mapped similarly.

Another set of challenges is involved with extraction of robust trajectories from unconstrained videos. Real word videos taken using handheld or mobile cameras contain camera motion and blur. On the other hand dynamic background, illuminated surfaces, non-separable foreground and background are very common. Fast moving objects also pose serious challenges to tracking. Tracking in unconstrained videos is a very active problem in Computer Vision.


ContributionBlok

We have defined and extracted features from the user sketches and videos which give us a qualitative understanding of the trajectories. There are four different type of features that we extract. We try to capture approximate shape, order and ditection of the trajectories and then combine them using a multilevel retrieval strategy. Our mutilevel retrieval strategy gives us a two-fold advantage.

  1. Firstly, it lets us combine the effect of multiple feature vectors of the same trajectory. The different feature vector captures different aspect of motion which is difficult.
  2. Secondly, temporal data suffers from the problem of unequal length feature vector. Our method handles this issue intelligently.
  3. Thirdly, our filters are arranged in increasing order of complexity and hence like a cascade they reduce the search space at each stage.

The adjacent figure explains our algorithm for retrieval. There are four different representations for the query and sketch. They are compared and matched at four different filters. The updated score is used for the final outcome.

Currently, we are focussing on extracting robust trajectories from unconstrained videos by improving some of the state of the art algorithms.

For a detailed description of the features used, please refer to our publication :
Koustav Ghosal, Anoop M. Namboodiri "A Sketch-Based Approach to Video Retrieval using Qualitative Features Proceedings of the Ninth Indian Conference on Computer Vision, Graphics and Image Processing, 14-17 Dec 2014, Bangalore, India.


Dataset

To validate our algorithm we had developed a dataset containing.

  1. A set of 100 Pool videos. Each video has been extracted from multiple International Pool Championship matches. All of them are top-view and HD videos
  2. A set of 100 Synthetic Videos. Each synthetic video represents a particular type of motion seen in real world scenarios.

Our datasets can be downloaded from the following links:

Please mail us at This email address is being protected from spambots. You need JavaScript enabled to view it. for the codes.


Results

queryExample

 

retrievedPool mrrpool 

 

 

 

 

 

 

 

 

 

 

 


Related Publications

  • Koustav Ghosal, Anoop M. Namboodiri - A Sketch-Based Approach to Video Retrieval using Qualitative Features Proceedings of the Ninth Indian Conference on Computer Vision, Graphics and Image Processing, 14-17 Dec 2014, Bangalore, India. [PDF]


Associated People

Relative Parts: Distinctive Parts for Learning Relative Attributes
 1 2 3
 

Abstract

The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as “smiling” for face images, “naturalness” for outdoor images, etc. For learning such attributes, a Ranking SVM based formulation was proposed that uses globally represented pairs of annotated images. In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories.First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive “significance coefficient” that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method , the new method is shown to achieve significant improvements in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.


CONTRIBUTIONS

  • Extend the idea of relative attributes (Parikh and Grauman ) to localized parts.
  • For each part we associate a locally adaptive “significance coefficient” that are learned simultaneously with a max-margin ranking model in an iterative manner.
  • Our method gives significant improvement (more than 10% on absolute scale) compared to the baseline method.
  • Introduce a new LFW-10 data set that has 10,000 pairs with instance-level annotations for 10 attributes.
  • Demonstrate application to interactive image search.

Method

methodoverview      

Dataset

We randomly select 2000 images from LFW dataset . Out of these, 1000 images are used for creating training pairs and the remaining (unseen) 1000 for testing pairs. The annotations are collected for 10 attributes, with 500 training and testing pairs per attribute. In order to minimize the chances of inconsistency in the dataset, each image pair is got annotated from 5 trained annotators, and final annotation is decided based on majority voting.

LFW10

code

Relative Parts: Distinctive Parts for Learning Relative Attributes (CVPR, 2014) 


Publication

Relative Parts: Distinctive Parts for Learning Relative Attributes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014


Results

learnedparts

Top 10 parts learned using our method with maximum weights for each of the ten attributes in LFW-10 dataset. Greater is the intensity of red, more important is that part, and vice-versa.

allaccuracies

Performance for each of the ten attributes in LFW-10 dataset using different methods and representations.


People


Acknowledgement

Yashaswi Verma is partly supported by MSR India PhD Fellowship 2013.


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