Multi-object tracking with weak appearances based on the surveillance video is one important issue for intelligent biology laboratory. However， due to the occlusion and subtle differences among objects， missing detection or false detection is prone to cause tracking failure. In addition， computational cost of deep learning is too high to be realized on embedded platforms. Therefore， a new lightweight multi-objects tracking algorithm is proposed， which uses YOLOv3 as the basic object detection network. A batch normalization layer weight evaluation based layer compression pruning algorithm is proposed to reduce the computational cost of the detection network such that the detection speed can be significantly improved on the embedded platform. Besides， according to the previous tracking results， the missing detection results can be corrected for the current frame， which improves the accuracy of the detection results. Furthermore， the convolutional neural network is employed to extract the object features. Object features and intersection-over-union （IoU） between the candidate frame and the prediction frame are combined for data association. Experimental results show that the proposed lightweight multi-object tracking algorithm achieves a better result compared with others. Especially， the network achieves a high compression rate with only slightly reducing the detection accuracy， which ensures the proposed network can be easily implemented on the embedded platform.