Fig. 1. Tracking drift using several classic tracking methods. (a) Algorithm in Ref. [9]; (b) algorithm in Ref. [14]; (c) algorithm in Ref. [15]; (d) algorithm in Ref. [16]

Fig. 2. Infrared images dataset (examples). (a) Bicycle; (b) bus; (c) car; (d) motorbike; (e) pedestrian

Fig. 3. Training loss versus number of iterations

Fig. 4. Test accuracy versus number of iterations

Fig. 5. Decision-level fusion tracking for infrared and visible spectra based on deep learning

Fig. 6. Process of decision-level fusion tracking

Fig. 7. Tracking results(frame sequence number is 1, 15, 32, 55, 129, 143, 162). (a) Infrared tracking; (b) visible tracking; (c) fusion tracking infrared and visible

Fig. 8. Comparison of overlap score between dual-band fusion and single band tracking

Fig. 9. Comparison of centre location error between dual-band fusion and single band tracking