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Performance evaluation on TLD dataset measured by Precision / Recall / F-measure

Sequence |Frames |OpenTLD(MatLab) | # 2 | # 3 | # 4 | # 5 | #6 | #7 :-----: |:-----: |:-----------: | :-----------: | : --: | :--: | :--: | :--:| :--: 1.David |761 |1.00 / 1.00 / 1.00|0.99 / 0.99 / 0.99|1.00 / 1.00 / 1.00|BAD|0.99 / 0.99 / 0.99| 0.99 / 0.99 / 0.99 | 0.95 / 0.95 / 0.95 2.Jumping |313 |1.00 / 1.00 / 1.00|0.85 / 0.85 / 0.85|0.92 / 0.92 / 0.92|0.79 / 0.88 / 0.83|1.00 / 0.996 / 0.998 | 0.95 / 0.94 / 0.95 | 1.00 / 0.997 / 0.998 3.Pedestrian 1|140 |1.00 / 1.00 / 1.00|0.52 / 0.42 / 0.47|0.68 / 0.55 / 0.61|1.00 / 0.65 / 0.79|1.00 / 0.99 / 0.996 | BAD | 1.00 / 0.65 / 0.79 4.Pedestrian 2|338 |0.89 / 0.92 / 0.91|0.47 / 0.41 / 0.44|1.00 / 0.30 / 0.46|-|0.93 / 0.97 / 0.95 | BAD | 0.84 / 0.38 / 0.52 5.Pedestrian 3|184 |0.99 / 1.00 / 0.99|1.00 / 0.41 / 0.58|1.00 / 0.42 / 0.59|-|0.97 / 0.50 / 0.66 | - |0.99 / 1.00 / 0.99 6.Car |945 |0.92 / 0.97 / 0.94 |0.92 / 0.99 / 0.95|0.94 / 0.98 / 0.96|0.92 / 0.99 / 0.95|0.93 / 0.97 / 0.95 | - | 0.93 / 0.995 / 0.96 7.Motocross |2665 |0.89 / 0.77 / 0.83| - | - | - | 0.72 / 0.84 / 0.78 | 0.86 /0.85 / 0.86 1 | 0.89 / 0.81 / 0.85 8.Volkswagen|8576 |0.80 / 0.96 / 0.87| - | - | - | 0.76 / 0.96 / 0.85 2 | 0.83 / 0.999 / 0.91 3 | 0.93 / 0.87 / 0.90 9.Carchase |9928 |0.86 / 0.70 / 0.77| - | - | - | - |0.78 / 0.78 / 0.78 4 | 0.86 / 0.59 / 0.70 10.Panda |3000 |0.58 / 0.63 / 0.60| - | - | - | 0.49 / 0.53 / 0.51 | 0.57 / 0.62 / 0.59 5 |0.72 / 0.79 / 0.76

Footnotes

  1. This is not the latest result. It obtained before clearing up. ↩

  2. This is not the latest result. (Runned with small size of NN examples set) ↩

  3. This is not the latest result. It obtained before clearing up. ↩

  4. This is not the latest result. It obtained before clearing up. ↩

  5. This is not the latest result. It obtained before clearing up. ↩