towards perspective-free object counting with deep learning
TRANSCRIPT
Towards perspective-free object counting withdeep learning
Daniel Onoro-Rubio and Roberto J. Lopez-Sastre
GRAM, University of Alcala, Alcala de Henares, [email protected] [email protected]
We include here some of the qualitative results obtained by our models forTRANCOS [1] and UCF CC 50 [2]. We show the results of our CCNN and Hydramodels.
Prediction: 41.4 Ground truth: 40.4 Prediction: 26.0 Ground truth: 29.0 Prediction: 31.1 Ground truth: 34.6
Prediction: 27.8 Ground truth: 29.5 Prediction: 21.7 Ground truth: 49.5 Prediction: 20.7 Ground truth: 27.4
Prediction: 31.9 Ground truth: 49.2 Prediction: 32.8 Ground truth: 30.5 Prediction: 31.6 Ground truth: 32.0
Prediction: 45.6 Ground truth: 43.6 Prediction: 40.8 Ground truth: 34.4 Prediction: 56.4 Ground truth: 56.0
Prediction: 65.8 Ground truth: 63.7 Prediction: 73.4 Ground truth: 63.7 Prediction: 58.9 Ground truth: 68.4
Fig. 1. Qualitative results for our CCNN model in the TRANCOS dataset.
2 Daniel Onoro-Rubio and Roberto J. Lopez-Sastre
Prediction: 30.1 Ground truth: 36.5 Prediction: 74.8 Ground truth: 79.3 Prediction: 39.8 Ground truth: 35.3
Prediction: 74.5 Ground truth: 63.7 Prediction: 58.1 Ground truth: 51.4 Prediction: 44.5 Ground truth: 48.9
Prediction: 34.6 Ground truth: 38.6 Prediction: 42.9 Ground truth: 41.7 Prediction: 11.3 Ground truth: 18.3
Prediction: 52.3 Ground truth: 49.7 Prediction: 17.1 Ground truth: 13.5 Prediction: 30.4 Ground truth: 34.5
Prediction: 50.9 Ground truth: 56.5 Prediction: 38.1 Ground truth: 38.7 Prediction: 44.9 Ground truth: 38.9
Fig. 2. Hydra 3s qualitative results for TRANCOS dataset.
Prediction: 2137.2 Ground truth: 4640.2 Prediction: 1547.7 Ground truth: 1944.2Prediction: 2398.9 Ground truth: 2368.1
Prediction: 1651.4 Ground truth: 2549.3Prediction: 822.5 Ground truth: 651.1
Prediction: 2200.9 Ground truth: 3407.0
Prediction: 1239.8 Ground truth: 483.9Prediction: 1777.1 Ground truth: 580.5
Prediction: 783.4 Ground truth: 730.4
Prediction: 2265.8 Ground truth: 2715.0 Prediction: 717.6 Ground truth: 968.2 Prediction: 1170.7 Ground truth: 1567.0
Prediction: 1015.7 Ground truth: 1859.3
Prediction: 1764.9 Ground truth: 1603.5Prediction: 586.4 Ground truth: 312.0
Fig. 3. Qualitative results in the UCF CC 50 dataset and our CCNN model.
Towards perspective-free object counting with deep learning 3
Prediction: 606.6 Ground truth: 440.3Prediction: 2780.2 Ground truth: 2374.3 Prediction: 2167.1 Ground truth: 1965.9
Prediction: 1413.5 Ground truth: 1454.5 Prediction: 2455.5 Ground truth: 2362.6
Prediction: 395.9 Ground truth: 330.1
Prediction: 1225.7 Ground truth: 1287.0 Prediction: 381.2 Ground truth: 163.8Prediction: 1292.3 Ground truth: 917.8
Prediction: 1196.5 Ground truth: 1116.0Prediction: 1841.7 Ground truth: 1999.5 Prediction: 1157.7 Ground truth: 1603.5
Prediction: 1449.8 Ground truth: 1859.3
Prediction: 2125.7 Ground truth: 2393.3Prediction: 1321.6 Ground truth: 1567.0
Fig. 4. Qualitative results produced by our Hydra 2s model in the UCF CC 50 dataset.
4 Daniel Onoro-Rubio and Roberto J. Lopez-Sastre
References
1. Guerrero-Gomez-Olmedo, R., Torre-Jimenez, B., Lopez-Sastre, R., Maldonado-Bascon, S., Onoro Rubio, D.: Extremely overlapping vehicle counting. In: IberianConference on Pattern Recognition and Image Analysis (IbPRIA). (2015)
2. Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting inextremely dense crowd images. In: CVPR. (2013)