In the process of panoramic video target tracking

the target deformation and scale changes caused by light change

interference of similar background

and object moving may result in target drift or missing

leading to low success rate and poor robustness. To address these issues

a target tracking method based on long short-term memory (LSTM) network and improved Real-Time MDNet (RT-MDNet) network was proposed. First

shallow convolution neural network was utilized to extract features

and adaptive RoIAlign was adopted to reduce pixel loss in the convolution process. Then

the weight of the last layer of the full connection layers was updated online by utilizing the target features to achieve foreground background separation and extract the target area. Lastly

the scale of the target box was selected adaptively by means of LSTM

and the target position information was thus obtained. Experimental results show that monocular vision algorithm could hardly adapt to the scale change and background change when applied in panoramic dataset

while the proposed method that utilizes 3-layer LSTM network to construct scale prediction module could effectively solve these problems. The algorithm can efficiently deal with the situations of small target

target occlusion

and cross motion of multiple targets in target tracking while maintaining accuracy

achieving better visual effect and higher overlap rate score.