Fig. 1. Framework of amStaple algorithm

Fig. 2. Tracking results of all thresholds ε on different benchmarks. (a) Precision curves; (b) success rate curves

Fig. 3. Tracking results of nine algorithms on different benchmarks. (a) Precision for OTB-2013 benchmark; (b) success rates for OTB-2013 benchmark; (c) precision for OTB-100 benchmark; (d) success rates for OTB-100 benchmark

Fig. 4. Seven attributes in OTB-100 benchmark sequence where amStaple has best performance. (a) Illumination variation; (b) out-of-plane rotation; (c) occlusion; (d) deformation; (e) in-plane rotation; (f) out-of-view; (g) background clutter

Fig. 5. Qualitative comparison of top three algorithms on ten videos. (a) Bird1; (b) Bolt; (c) ClifBar; (d) coke; (e) dog; (f) dragon baby; (g) shaking; (h) girl; (i) soccer; (j) trellis

Fig. 6. Video attribute success rate curves where amStaple1 performs better than amStaple on different benchmarks. (a) Fast motion for OTB-2013 benchmark; (b) motion blur for OTB-2013 benchmark; (c) low resolution for OTB-2013 benchmark; (d) fast motion for OTB-100 benchmark; (e) motion blur for OTB-100 benchmark; (f) low resolution for OTB-100 benchmark

Fig. 7. Tracking effects of amStaple1 and amStaple in Deer benchmark sequence. Red is amStaple and green is amStaple1

Table 1. Basic experimental parameter settings

Table 2. Precision and success rates of amStaple and Staple on different benchmarks and percentage increases of amStaple compared to Staple

Table 3. Comparison of success rates of amStaple and Staple on different attributes of OTB-100 benchmark