Nowadays, due to its many applications, objects detection in wide area motion imagery (WAMI) sequences has received a lot of attention. Unlike natural images, object detection in WAMI faces unique challenges. Lack of appearance information due to the small size of objects makes object detection difficult for conventional methods. In addition, pixel noise, registration errors, sparse or densely populated objects, brings on pronounced artifacts which amplifies the difficulty of detection. This paper aims to address object detection problem in the presence of these issues by considering objects as keypoints in the relevant background and proposes a spatiotemporal anchor-free detector for tiny vehicles in WAMI images. Instead of background subtraction, a region of interest network refines large search space of sequences to indicates object clusters. For further investigation, clusters are encoded by a codebook which is learned through an unsupervised encoder-decoder network. To accurately generate the detections, a Transformer network is trained on cluster embeddings using groundtruth heatmaps that are described by Gaussian distribution rather than hard label annotation. The network is trained with a redesigned version of Focal loss comprising a shape prior regularizer which help the generated heatmaps to conform to the shape of the keypoints. Extensive experiments on WPAFB dataset demonstrate the high capability of our method for the detection of small vehicles where it achieves competitive performance when compared to the state-ofthe-art.