(number of frames that satisfies the condision)/(number of total frames) ×100% (11)
Table 1 shows the performance of the proposed detection and tracking systems. Tables 2 and 3 show comparisons of the proposed method with other tracking algorithms.
Table 1. Performance of the proposed algorithm with five datasets
Name of video sequences Frame Overlap FPS
Sitcom 3,898 41.4 24
NissanSkyLineCC 3,742 91.2 27
Lemming 1,335 96.3 25
Basketball 725 89.5 26
Skating 707 19.4 23
Table 2. Showing the percent of the frames for which the PASCAL criterion was a_0>0.5 
Name of video sequences IVT OAB MIL VTF LOT Proposed Method
Skating 3.8 8.8 9.8 11.5 29.4 19.4
Lemming 16.2 37.1 37.6 54.3 73.8 96.3
Table 3. Comparing to Matrioska tracker with LTDT sequences 
Name of video sequences Frame Matrioska Overlap Proposed algorithm Overlap
Sitcom 3,742 89.4 91.2
NissanSkyLineCC 3,898 53.4 41.4
The proposed tracking algorithm achieved improved result from NissanskylineCC and Lemming sequences and achieved parity with the other tracking methods with other datasets. The proposed approach outperforms the others in the similar background and severe scale change states. The severe appearance changes, such as skating, show the lowest overlap. However, the proposed algorithm was robust in difficult states such as appearance changes, changes scale, covered object, and similar background. “The Matrioska Tracking Algorithm on LTDT2014 Dataset”  and “Locally Orderless…
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