1. A method for enhancing target tracking via detector and tracker fusion for Unmanned Aerial Vehicles (UAVs), comprising: receiving at least one raw input image of objects to be detected; based on the at least one raw input image of objects, generating the objects' candidate information; based on the objects' candidate information, calculating location and velocity estimation of an object at a current timestamp based on a detector and tracker fusion, comprising: generating a binary feature canvas; processing the binary feature canvas based on an attention mechanism to focus on relevant parts of the binary feature canvas; extracting features of the objects from the processed binary feature canvas; and calculating the location and velocity estimation of the object at the current timestamp; wherein processing the binary feature canvas based on the attention mechanism to focus on relevant parts of the binary feature canvas further including: generating an attention mask M t (e t-1 ) and applying the attention mask M t (e t-1 ) to each channel of the feature canvas c t , respectively; and obtaining a masked feature canvas, wherein the attention mask M t (e t-1 ) is generated by a mixture of N×N Gaussians, each Gaussian (i,j) has a center at ( rx t - 1 + lx t - 1 2 + vx t - 1 + ( i - N 2 - 0.5 ) , ⁢ ry t - 1 + ly t - 1 2 + vy t - 1 + ( i - N 2 ) ) ⁢ ⁢ and ⁢ ⁢ σ = S frame S Pt - 1 , where S frame denotes an area of the raw input image, and S p t-1 denotes an area of the object's estimated bounding box at a timestamp (t−1), and m(c t , e t-1 )=M t (e t-1 )·c t , where m(c t , e t-1 ) denotes the masked feature canvas; and based on the location and velocity estimation of the object at the current timestamp, predicting the location and velocity estimation of the object at a future timestamp.