Fig. 1. Scale change of target motion in panoramic video

Fig. 2. Panoramic image stitched by seven cameras

Fig. 3. Visualization results of deep features in convolutional neural networks. (a) Conv 1; (b) Relu 1; (c) Pool 1; (d) Conv 3; (e) Relu 3; (f) Pool 3; (g) Conv 5; (h) Relu 5; (i) original image

Fig. 4. Network architecture of proposed algorithm

Fig. 5. Convolution structure in MobileNetV3

Fig. 6. Comparison among Relu6, h-swish, and swish activation functions

Fig. 7. Curve of loss function

Fig. 8. Comparison of experiment results by SiameseRPN and improved network

Fig. 9. Comparison of results by different algorithms in four different scenarios

Fig. 10. Experimental results for small targets and target occlusion

Fig. 11. Experimental results for multi-target cross movements

Fig. 12. Experimental results for similar target interference

Fig. 13. Test results of six algorithms on panoramic dataset. (a) Precision; (b) success rate