Path Planning Algorithms -

https://github.com/conq44/Where-to-sire.git

Recursive and Sampling based approaches

Simple RL based path planning (not generalized)

Multi-object tracking with Trackletnet

https://arxiv.org/pdf/1811.07258.pdf 

An attempt at utilizing trackletnet for the visdrone dataset was made, local training/fine-tuning became too expensive to continue and I plan to pursue a simpler MOT algorithm coded from scratch soon. 

In any case here's how it works in simple words- 

A tracklet is an association between what the model thinks is the same object in multiple frames. This is what allows us to track the same object over the video sequence. In this paper the authors essentially propose a graph model in which the different tracklets are the nodes in the graph and the similarity between the different tracklets are the edges. First the objects in every frame are cropped using the ground truth labels and an appearance model is trained. This repo uses the facenet architecture to train using triplet loss. To train the model every cropped object (the anchor) is paired with other objects which are either the same object in a different frame (positive) or a different object(negative). The model is then trained to identify which objects are the same and which are different. An important thing to consider when training this type of model in sequence data is that the validation data must be taken from a different video sequence to check the performance of the model on an unseen object set. The Intersection over Union of the tracklets from their epipolar geometry is also used. Epipolar geometry helps to differentiate between different objects occupying the same spot in consecutive frames in which case simple IOU would identify them as the same object. This step allows to build tracklets form the video sequences. These tracklets form the set V. Keep in mind that there maybe multiple tracklets that are derived from the same starting object and many of these might be very similar.

Next the custom CNN called trackletnet is used to identify similarity between pairs of tracklets from the set V. This then forms the edges of the graph in set E. Clustering is then carried out on the graph and final tracklets are output as a result. These tracklets identify the same objects in each video sequence.