Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, Ergys and Solera, Francesco and Zou, Roger S. and Cucchiara, Rita and Tomasi, Carlo
2016

Paper summary
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In this paper, the authors present a new measure for evaluating person tracking performance and to overcome problems when using other event-based measures (MOTA - Multi Object Tracking Accuracy, MCTA - Multi Camera Tracking Accuracy, Handover error) in multi-camera scenario.
The emphasis is on maintaining correct ID for a trajectory in most frames instead of penalizing identity switches. This way, the proposed measure is suitable for MTMC (Multi-target Multi-camera) setting where the tracker is agnostic to the true identities.
They do not claim that one measure is better than the other, but each one serves a different purpose. For applications where preserving identity is important, it is fundamental to have measures (like the proposed ID precision and ID recall) which evaluate how well computed identities conform to true identities, while disregarding where or why mistakes occur. More formally, the new pair of precision-recall measures ($IDP$ and $IDR$), and the corresponding $F_1$ score $IDF_1$ are formulated as:
\begin{equation}
IDP = \dfrac{IDTP}{IDTP+IDFP}
\end{equation}
\begin{equation}
IDR = \dfrac{IDTP}{IDTP+IDFN}
\end{equation}
\begin{equation}
IDF_1 = \dfrac{2 \times IDTP}{2 \times IDTP + IDFP + IDFN}
\end{equation}
where $IDTP$ is the True Positive ID, $IDFP$ is the False Positive ID, and $IDFN$ is the False Negative ID for every corresponding association.
Another contribution of the paper is a large fully-annotated dataset recorded in an outdoor environment. Details of the dataset: It has more than $2$ million frames of high resolution $1080$p,$60$fps video, observing more than $2700$ identities and includes surveillance footage from $8$ cameras with approximately $85$ minutes of videos for each camera. The dataset is available here: http://vision.cs.duke.edu/DukeMTMC/.
Experiments show that the performance of their reference tracking system on another dataset (http://mct.idealtest.org/Datasets.html), when evaluated with existing measures, is comparable to other MTMC trackers. Also, a baseline framework on their data is established for future comparisons.
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

Ristani, Ergys and Solera, Francesco and Zou, Roger S. and Cucchiara, Rita and Tomasi, Carlo

European Conference on Computer Vision - 2016 via Local Bibsonomy

Keywords: dblp

Ristani, Ergys and Solera, Francesco and Zou, Roger S. and Cucchiara, Rita and Tomasi, Carlo

European Conference on Computer Vision - 2016 via Local Bibsonomy

Keywords: dblp

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