ACIVS 2013 conference poster presentation

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1) The document evaluates the performance of video analytics software for surveillance on trains, comparing commercial and open-source implementations. 2) It presents frame-based and object-based metrics to measure the performance of algorithms in detecting objects, tracks, false alarms, and other factors. 3) The results show that the commercial system detected objects more reliably with far fewer false alarms and better preservation of object tracks compared to the open-source system.

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PERFORMANCE EVALUATION OF VIDEO ANALYTICS
FOR SURVEILLANCE ON-BOARD TRAINS
Context and Objectives
ACIVS’13, October 30th 2013, Poznan, Poland
Low level Evaluation
Real-time video surveillance is used in public transportation, including on-board metro
trains, to support human operators in control rooms.
Video Content Analytics (VCA) is effective when the performance is such to reduce
false alarms under appropriate acceptability thresholds.
Reducing the causes of false alarms by fine tuning the VCA is particularly important in
on-board applications, that are more prone to disturbances with respect to fixed
installations due to more critical operating conditions (e.g. constrained camera
field of view, frequent light changes, vibrations, occlusions, etc.).
Accurate performance evaluation is essential to decide about:
 Which alarms to activate depending on scenarios
 How to set algorithm parameters in each scenario
Comparison between commercial and open-source implementations is also important
to evaluate the added value provided by the former in terms of achievable
performance.
Black Box Evaluation
Valentina Casola, Mariana Esposito, Francesco Flammini, Nicola Mazzocca, Concetta Pragliola
Contributions
Evaluation of Commercial Off-The-Shelf (COTS) VCA system
Low Level Black Box
Alarm Performance Evaluation
Comparison with Open source
System
Frame and Object Based Metrics
Ground Truth values compared with
Algorithm Result
Computed by specific tool developed in
Matlab
Object Based Metrics
Consider the whole trajectory of each
object in the scene and preserves its
lifetime
Correct Detected Track
(CDT)
Track Detection Failure
(TDF)
False Alarm Track
(FAT)
Track Fragmentation
(TF)
ID Change (IDC)
Temporal Overlap
Spatial Overlap
Performance Evaluation ToolAlgorithm Result
Ground Thruth
Tot Tos
Metrics
(15%)
Frame Based Metrics
Measure the performance on individual
frames of a video stream and do not
consider the preservatio. The blobs are
evaluated in their size and location and
compared with GT.
True Positive (TP)
False Negative (FN):
Fragmentation
ecall
False Positive (FP)
Merging
Precision
0
50
100
150
200
250
300
350
400
Occurrencies
#AR TP FN FP TF IDC
Object-Based Results
COTS
Open-Source
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
PerformanceIndex
Accuracy FAR PP FNR
Object-Based Performance
COTS
Open-Source
False Alarm Rate FPTP
FP

FAR
Detection rate
FNTP
TP

DR
Positive Prediction
FPTP
TP

PP
False Negative Rate TPFN
FN

FNR
Fragmentation Index FRAGM
TP
FI
Merge Index MERGE
FPOBJAR 
FM
♦ 1510 objects in 600 frames, 8 GT tracks
♦ COTS system detects less objects but
it is far more reliable regarding false
positives (84 vs 2168)
♦ FA of 6% instead of 41% (the latter
clearly unacceptable).
♦ COTS object Fragmentation is less
then one half w.r.t. Open source.
Metrics COTS Open
TP 1307 1324
FN 203 186
FP 84 2168
Metrics COTS Open
TP 7 8
FN 1 0
FP 9 213
TF 11 17
IDC 0 0
♦ 1510 objects in 600 frames, 8 GT tracks
♦ COTS FP and PP reveal higher reliability
♦ Same consideration for TF (important
for event persistence).
Performance Indices
Test of COTS system on real
vehicle for light railwails and
tramways
False Alarm Index
RealAlarms#
sFalseAlarm#
FAI 
Probability Of Detection
PDTPFN
TP

POD
over it detects camera occlusionblurring with paint or obstacles.
amper it detects the manumission of the camera by moving it.
top it detects still objects in the scene.
Presence it detects objectspeople moving in the scene
rowd it raises an alarm when the scene is overcrowded.
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1. PERFORMANCE EVALUATION OF VIDEO ANALYTICS FOR SURVEILLANCE ON-BOARD TRAINS Context and Objectives ACIVS’13, October 30th 2013, Poznan, Poland Low level Evaluation Real-time video surveillance is used in public transportation, including on-board metro trains, to support human operators in control rooms. Video Content Analytics (VCA) is effective when the performance is such to reduce false alarms under appropriate acceptability thresholds. Reducing the causes of false alarms by fine tuning the VCA is particularly important in on-board applications, that are more prone to disturbances with respect to fixed installations due to more critical operating conditions (e.g. constrained camera field of view, frequent light changes, vibrations, occlusions, etc.). Accurate performance evaluation is essential to decide about:  Which alarms to activate depending on scenarios  How to set algorithm parameters in each scenario Comparison between commercial and open-source implementations is also important to evaluate the added value provided by the former in terms of achievable performance. Black Box Evaluation Valentina Casola, Mariana Esposito, Francesco Flammini, Nicola Mazzocca, Concetta Pragliola Contributions Evaluation of Commercial Off-The-Shelf (COTS) VCA system Low Level Black Box Alarm Performance Evaluation Comparison with Open source System Frame and Object Based Metrics Ground Truth values compared with Algorithm Result Computed by specific tool developed in Matlab Object Based Metrics Consider the whole trajectory of each object in the scene and preserves its lifetime Correct Detected Track (CDT) Track Detection Failure (TDF) False Alarm Track (FAT) Track Fragmentation (TF) ID Change (IDC) Temporal Overlap Spatial Overlap Performance Evaluation ToolAlgorithm Result Ground Thruth Tot Tos Metrics (15%) Frame Based Metrics Measure the performance on individual frames of a video stream and do not consider the preservatio. The blobs are evaluated in their size and location and compared with GT. True Positive (TP) False Negative (FN): Fragmentation ecall False Positive (FP) Merging Precision 0 50 100 150 200 250 300 350 400 Occurrencies #AR TP FN FP TF IDC Object-Based Results COTS Open-Source 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 PerformanceIndex Accuracy FAR PP FNR Object-Based Performance COTS Open-Source False Alarm Rate FPTP FP  FAR Detection rate FNTP TP  DR Positive Prediction FPTP TP  PP False Negative Rate TPFN FN  FNR Fragmentation Index FRAGM TP FI Merge Index MERGE FPOBJAR  FM ♦ 1510 objects in 600 frames, 8 GT tracks ♦ COTS system detects less objects but it is far more reliable regarding false positives (84 vs 2168) ♦ FA of 6% instead of 41% (the latter clearly unacceptable). ♦ COTS object Fragmentation is less then one half w.r.t. Open source. Metrics COTS Open TP 1307 1324 FN 203 186 FP 84 2168 Metrics COTS Open TP 7 8 FN 1 0 FP 9 213 TF 11 17 IDC 0 0 ♦ 1510 objects in 600 frames, 8 GT tracks ♦ COTS FP and PP reveal higher reliability ♦ Same consideration for TF (important for event persistence). Performance Indices Test of COTS system on real vehicle for light railwails and tramways False Alarm Index RealAlarms# sFalseAlarm# FAI  Probability Of Detection PDTPFN TP  POD over it detects camera occlusionblurring with paint or obstacles. amper it detects the manumission of the camera by moving it. top it detects still objects in the scene. Presence it detects objectspeople moving in the scene rowd it raises an alarm when the scene is overcrowded. ALARMS

Editor's Notes

Recall implementata Precision implementata • Calcola, in ogni frame, il numero di oggetti di GT rilevati correttamente dal sistema e, per ognuno di essi, il numero di bounding-box in cui sono stati frammentati  True Positive (TP)  False Negative (FN)  Fragmentation(FRAGM) • Calcola, in ogni frame, il numero di oggetti di AR che non corrispondono ad alcun oggetto di GT. Per quelli che risultano associati ad oggetti di GT ricava, invece, il numero esatto di questi ultimi  False Positive (FP)  Merging (MERGE)

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