ICPR - HARL 2012 - Results

The participants

70 teams have downloaded our dataset. 4 participants submitted results:

Nr. Authors and team affilation Description Short team name Choosen dataset Loc. provided
49 Bingbing Ni and Yong Pei
Advanced Digital Sciences Center, Singapore

Jun Tan, Jian Dong and Shuicheng Yan
National University of Singapore, Singapore

Pierre Moulin
University of Illinois at Urbana-Champaign
Description ADSC-NUS-UIUC D1 Yes
59 Dr. Tanushyam Chattopadhyay, Sangheeta Roy and Aniruddha Sinha
Innovation Lab, Tata Consultancy Services, Kolkata

Prof. Dipti Prasad Mukherjee and Apurbaa Mallik
Indian Statistical Institute, Kolkata
Description TATA-ISI D1 No
13 Juan C. SanMiguel and Sergio Suja
Video Processing and Understanding Lab
Universidad Autonoma of Madrid, Spain
Not available VPULABUAM D2 Yes
51 Yonghao He , Hao Liu , Wei Sui, Shiming Xiang and Chunhong Pan
Institute of Automation,
Chinese Academy of Sciences, Beijing
Description IACAS D2 Yes

Performance results

For a detailed description of the evaluation metrics is available here.

Pure detection and recognition performance - no localization

The first performance measure ignores the localization information of the results (frame number and bounding boxes) and only provides information on the pure detection and recognition performance through the classical Precision and Recall measures from information retrieval.

Recall is the number of correctly detected and recognized actions with respect to the number of actions in the ground truth. A recall of 100% means that ALL groundtruth actions have been found and correctly recognized.

Precision is the number of correctly detected and recognized actions with respect to the number of detected actions in the result set. A precision of 100% means that no additional actions have been found other than the ones in the ground truth (no false alarms).

The F-Score is defined as the harmonic mean of precision and recall : F=2*P*R/(P+R).

Nr.TeamDataset RecallPrecisionF-Score
49ADSC-NUS-UIUC D1 0.74 0.41 0.53
59TATA-ISI D1 0.08 0.17 0.11
13VPULABUAM D2 0.36 0.66 0.46
51IACAS D2 0.30 0.46 0.36

Detection, recognition and localization performance at 10% quality level

When localization information is taken into account, determining whether an action is correctly detected requires to set up thresholds on the amount of overlap between the groundtruth action and the detected action. We refer to evaluation metric for more details. In the following table we give precision and recall values for a fixed threshold of 10%, i.e. a groundtruth action is matched to a detected action if

Nr.TeamDataset RecallPrecisionF-Score
49ADSC-NUS-UIUC D1 0.63 0.33 0.44
13VPULABUAM D2 0.04 0.08 0.05
51IACAS D2 0.03 0.04 0.03

Detection, recognition and localization performance : integrated quality

Here we report integrated performance, as described in our evaluation metric. The four quality thresholds rt, pt, rs, pt are changed and F-score is integrated over the interval of possible values.

Nr.TeamDataset Rec_TPre-TRec-SPre-STotal
49ADSC-NUS-UIUC D1 0.27 0.37 0.29 0.37 0.33
13VPULABUAM D2 0.03 0.03 0.02 0.03 0.03
51IACAS D2 0.03 0.00 0.01 0.01 0.02

Performance vs. quality curves

Here we report the performance vs. quality curves described in our evaluation metric. Each diagram presents curves corresponding to precision, recall and F-Score over a varying quality threshold. One of the thresholds rt, pt, rs, pt varies over the x-axis, whereas the other 4 are kept fixed at 10%.

Varying rt
Varying pt
Varying rs
Varying ps

Raw data

The raw data for the plots above can be downloaded here:


Confusion matrices

Special care needs to be taken for the interpretation of the following confusion matrices. Only couples of matched ground truth / detection actions are included:


Description of the competing methods

List of competing methods