IEEEA　new　unified　video　analytics　framework　(ER3)　is　proposed　for　complex　event　retrieval,　recognition　and　recounting,　based　on　the　proposed　video　imprint　representation,　which　exploits　temporal　correlations　among　image　features　across　video　frames.　With　the　video　imprint　representation,　it　is　convenient　to　reverse　map　back　to　both　temporal　and　spatial　locations　in　video　frames,　allowing　for　both　key　frame　identification　and　key　areas　localization　within　each　frame.　In　the　proposed　framework,　a　dedicated　feature　alignment　module　is　incorporated　for　redundancy　removal　across　frames　to　produce　the　tensor　representation,　i.e.,　the　video　imprint.　Subsequently,　the　video　imprint　is　individually　fed　into　both　a　reasoning　network　and　a　feature　aggregation　module,　for　event　recognition/recounting　and　event　retrieval　tasks,　respectively.　Thanks　to　its　attention　mechanism　inspired　by　the　memory　networks　used　in　language　modeling,　the　proposed　reasoning　network　is　capable　of　simultaneous　event　category　recognition　and　localization　of　the　key　pieces　of　evidence　for　event　recounting.　In　addition,　the　latent　structure　in　our　reasoning　network　highlights　the　areas　of　the　video　imprint,　which　can　be　directly　used　for　event　recounting.　With　the　event　retrieval　task,　the　compact　video　representation　aggregated　from　the　video　imprint　contributes　to　better　retrieval　results　than　existing　state-of-the-art　methods.