Object Trajectory Analysis in Video Indexing and Retrieval Applications

Broilo, Mattia; Piotto, Nicola; Boato, Giulia; Conci, Nicola; Natale, Francesco G. B. De

doi:10.1007/978-3-642-12900-1_1

Cited by 18 publications

(14 citation statements)

References 82 publications

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“…An approach to represent shots by the time-varying directions of objects and the time-varying spatial relationships between objects is proposed by the systems in Li et al (1997), Nabil et al (1997 and Broilo et al (2010). For example, in Li et al (1997), a motion trajectory is considered as a set of points.…”

Section: Video Databasesmentioning

confidence: 99%

“…Also, at each point, the spatial relationships between objects are represented. However, these systems (Broilo et al 2010;Li et al 1997;Nabil et al 1997), only address an exact match algorithm for matching user queries with target shots.…”

Section: Video Databasesmentioning

confidence: 99%

“…Content-based search of sequences is a complex task and commercial systems, such as YouTube and Google Video, largely rely on pure textual search (Broilo et al 2010). However, some research works in video indexing and retrieval, such as (Flickner et al 1995;La Cascia and Ardizzone 1996;Smith and Chang 1996), have concentrated on extracting visual features (color, motion, shape, etc.)…”

Section: Introductionmentioning

confidence: 99%

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cTraj: efficient indexing and searching of sequences containing multiple moving objects

Aghbari

2011

J Intell Inf Syst

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Indexing sequences containing multiple moving objects by all features of these objects captured at every clock tick results in huge index structures due to the large number of extracted features in all sampled instances. Thus, the main problems with current systems that index sequences containing multiple moving objects are: huge storage requirements for index structures, slow search time and low accuracy due to lack of representation of the time-varying features of objects. In this paper, a technique called cTraj to address these problems is proposed. For each object in a sequence, cTraj captures the features at sampled instances. Then, it maps the object's features at each sampled instance from high-dimensional feature space into a point in low-dimensional distance space. The sequence of points of an object in lowdimensional space is considered the time-varying feature trajectory of the object. To reduce storage requirements of an index structure, the sequence of points in each trajectory is represented by a minimum bounding box (MBB). cTraj indexes a sequence by the MBBs of its objects using a spatial access method (SAM), such as an R−tree; thus, greatly reducing storage requirements of the index and speeding up the search time. The cTraj technique does not result in any false dismissal, but the result might contain a few false alarms, which are removed by a two-step refinement process. The experiments show that the proposed cTraj technique produces effective results comparable to those of a sequential method, however much more efficient.

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Section: Video Databasesmentioning

confidence: 99%

Section: Video Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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cTraj: efficient indexing and searching of sequences containing multiple moving objects

Aghbari

2011

J Intell Inf Syst

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“…The motion-based analysis of a video is frequent in surveillance, human-machine interaction, automatic target recognition and automotive applications [4]. Most existing approaches have primarily two phases in a CBVRS pipeline.…”

Section: Introductionmentioning

confidence: 99%

A Sketch-Based Approach To Video Retrieval Using Qualitative Features

Ghosal

Namboodiri

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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Motion trajectories extracted from certain videos contain sufficient spatio-temporal information which can be effectively used to characterize those videos. But the task of framing text-based queries for such videos in content-based video retrieval systems is very complicated. Sketch based query is an efficient tool to construct motion-based queries but perceptual differences like spatial and temporal variability pose serious challenges to query modelling.In this work we propose a new method of modelling sketch based queries which attempts to extract the qualitative features of motion by minimizing the perceptual variability. We also develop a multilevel filter for indexing a query, in which the search results are refined at each stage using a cumulative scoring mechanism. Finally, we show the effectiveness of our algorithm on a dataset of real pool videos and a synthetic dataset containing simulated videos having very complex motion trajectories.

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“…For example, in video trajectory analysis, the quantization technique is adopted in order to bring the observable information from a continuos to a symbolic level: after an initial stage of feature extraction, some signal quantization and symbolic coding is typically computed. Once the quantized discrete data is available, the similarity between sequences is evaluated adopting, among others, simple metrics (e.g., L p norms, Euler or Hausdorff distances, or string alignment algorithms) [5]. The authors of [6] present a complete framework for motion-based video retrieval in which they adopt the minimum cumulative square distance as a metric.…”

Section: Introductionmentioning

confidence: 99%

Boosting quantization for L<inf>p</inf> norm distortion measure

Wang

Piotto

Schonfeld

2012

2012 IEEE Statistical Signal Processing Workshop (SSP)

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Quantization is a widely used technique in signal processing. The purpose of many quantization schemes is to faithfully reproduce the input signals. However, in many situations, one is more interested in comparison of different classes of signals in order to classify them into different categories. The classification criterion is based on comparing distances under certain metric. For classical quantizers, although the individual quantized signal may show high fidelity to its original signals, the distance features characterizing different categories may not be well reserved, which results in poor performance of classification in spite of relatively good reproduction of individual signals. In this paper, we propose a special optimal quantization under the L p norm distortion measure called Boosting Quantization. The quantization is guaranteed to preserve the distances of different classes. We provide a quantization algorithm to generate the quantizer. We also derive several theoretical properties for the proposed quantization. Finally, we provide numerical examples to illustrate our proposed boosting quantization.

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Object Trajectory Analysis in Video Indexing and Retrieval Applications

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 18 publications

References 82 publications

cTraj: efficient indexing and searching of sequences containing multiple moving objects

cTraj: efficient indexing and searching of sequences containing multiple moving objects

A Sketch-Based Approach To Video Retrieval Using Qualitative Features

Boosting quantization for L<inf>p</inf> norm distortion measure

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