Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

Table 4

The tracking performance for EventSeq-Vehicle1 using different tracking methods (SORT, GM-PHD, GM-CPHD, and PDAF) with detection through DBSCAN clustering. The impact of time intervals from 10ms to 30ms was studied.

Tis	Tracker	MOTA	MOTP	MT	PT	ML	FP	FN	IDs	FM
10ms	SORT	36.2%	69.2%	8	79	20	2891	16369	146	1302
10ms	GM-PHD	24.0%	69.1%	1	85	21	4924	16649	1541	4097
10ms	GM-CPHD	21.1%	69.2%	3	89	15	7480	15621	900	3616
10ms	PDAF	20.9%	69.1%	0	86	21	5653	18228	158	4678
20ms	SORT	35.0%	70.2%	18	71	18	2905	6893	92	444
20ms	GM-PHD	35.1%	70.6%	18	70	19	2523	7019	323	770
20ms	GM-CPHD	25.7%	70.5%	12	75	20	3974	7180	152	716
20ms	PDAF	24.5%	70.4%	4	80	23	3576	7815	95	1371
30ms	SORT	28.5%	70.4%	12	69	26	1950	5190	94	265
30ms	GM-PHD	23.6%	70.8%	14	67	26	2323	5224	190	478
30ms	GM-CPHD	18.3%	70.7%	8	76	23	2870	5259	135	481
30ms	PDAF	19.3%	70.5%	1	76	30	2402	5701	66	900