Objective:　Object　detection　is　a　fundamental　task　in　computer　vision　applications,　which　provides　support　for　subsequent　object　tracking,　semantic　segmentation,　and　behavior　recognition.　Recent　years　have　witnessed　substantial　progress　in　still　image　object　detection　based　on　deep　convolutional　neural　network　(DCNN).　The　task　of　still　image　object　detection　is　to　determine　the　category　and　position　of　each　object　in　an　image.　Video　object　detection　aims　to　locate　a　moving　object　in　sequential　images　and　assign　a　specific　category　label　to　each　object.　The　accuracy　of　video　object　detection　suffers　from　degenerated　object　appearances　in　videos,　such　as　motion　blur,　multiobject　occlusion,　and　rare　poses.　The　methods　of　still　image　object　detection　achieve　excellent　results,　but　directly　applying　them　to　video　object　detection　is　challenging.　According　to　the　temporal　and　spatial　information　in　videos,　most　existing　video　object　detection　methods　improve　the　accuracy　of　moving　object　detection　by　considering　spatiotemporal　consistency　based　on　still　image　object　detection.　Method:　In　this　paper,　we　propose　a　video　object　detection　method　using　fusion　of　single　shot　multibox　detector　(SSD)　and　spatiotemporal　features.　Under　the　framework　of　SSD,　temporal　and　spatial　information　of　the　video　are　applied　to　video　object　detection　through　the　optical　flow　network　and　the　feature　pyramid　network.　On　the　one　hand,　the　network　combining　residual　network　(ResNet)　101　with　four　extra　convolutional　layers　is　used　for　feature　extraction　to　produce　the　feature　map　in　each　frame　of　the　video.　An　optical　flow　network　estimates　the　optical　flow　fields　between　the　current　frame　and　multiple　adjacent　frames　to　enhance　the　feature　of　the　current　frame.　The　feature　maps　from　adjacent　frames　are　compensated　to　the　current　frame　according　to　the　optical　flow　fields.　The　multiple　compensated　feature　maps　as　well　the　feature　map　of　the　current　frame　are　aggregated　according　to　adaptive　weights.　The　adaptive　weights　indicate　the　importance　of　all　compensated　feature　maps　to　the　current　frame.　Here,　the　cosine　similarity　metric　is　utilized　to　measure　the　similarity　between　the　compensated　feature　map　and　the　feature　map　extracted　from　the　current　frame.　If　the　compensated　feature　map　is　close　to　the　feature　map　of　the　current　frame,　then　the　compensated　feature　map　is　assigned　a　larger　weight;　otherwise,　it　is　assigned　a　smaller　weight.　Moreover,　an　embedding　network　that　consists　of　three　convolutional　layers　is　applied　on　the　compensated　feature　maps　and　the　current　feature　map　to　produce　the　embedding　feature　maps,　and　the　embedding　feature　maps　are　used　to　compute　the　adaptive　weights.　On　the　other　hand,　the　feature　pyramid　network　is　used　to　extract　multiscale　feature　maps　that　are　used　to　detect　the　object　of　different　sizes.　The　low-and　high-level　feature　maps　are　used　to　detect　smaller　and　larger　objects,　respectively.　For　the　problem　of　small　object　detection　in　the　original　SSD　network,　the　low-level　feature　map　is　combined　with　the　high-level　feature　map　to　enhance　the　semantic　information　of　the　low-level　feature　map　via　upsampling　operation　and　a　1×1　convolutional　layer.　The　upsampling　operation　is　used　to　extend　the　high-level　feature　map　to　the　same　resolution　as　the　low-level　feature　map,　and　the　1×1　convolution　layer　is　used　to　reduce　the　channel　dimensions　of　the　low-level　feature　map　to　be　consistent　with　those　of　the　high-level　feature　map.　Then,　multiscale　feature　maps　are　input　into　the　detection　network　to　predict　bounding　boxes,　and　nonmaximum　suppression　is　carried　out　to　filter　the　redundant　bounding　boxes　and　obtain　the　final　bounding　boxes.　Result:　Experimental　results　show　that　the　mean　average　precision　(mAP)　score　of　the　proposed　method　on　the　ImageNet　VID(ImageNet　for　video　object　detection)　dataset　can　reach　72.0%,　which　is　24.5%,　3.6%,　and　2.5%　higher　than　those　of　the　temporal　convolutional　network,　the　method　combining　tubelet　proposal　network　with　long　short　memory　network,　and　the　method　combining　SSD　and　siamese　network,　respectively.　In　addition,　an　ablation　experiment　is　conducted　with　five　network　structures,　namely,　16-layer　visual　geometry　group(VGG16)　network,　ResNet101　network,　the　network　combining　ResNet101　with　feature　pyramid　network,　and　the　network　combining　ResNet101　with　spatiotemporal　fusion.　The　network　structure　combining　ResNet101　with　spatiotemporal　fusion　improves　the　mAP　score　by　11.8%,　7.0%,　and　1.2%　compared　with　the　first　four　network　structures.　For　further　analysis,　the　mAP　scores　of　the　slow,　medium,　and　fast　objects　are　reported　in　addition　to　the　standard　mAP　score.　Our　method　combined　with　optical　flow　improves　the　mAP　score　of　slow,　medium,　and　fast　objects　by　0.6%,　1.9%,　and　2.3%,　respectively,　compared　with　the　network　structure　combining　ResNet101　with　feature　pyramid　network.　Experimental　results　show　that　the　proposed　method　can　improve　the　accuracy　of　video　object　detection,　especially　the　performance　of　fast　object　detection.　Conclusion:　Temporal　and　spatial　correlation　of　the　video　by　spatiotemporal　fusion　are　used　to　improve　the　accuracy　of　video　object　detection　in　the　proposed　method.　Using　the　optical　flow　network　in　video　object　detection　can　compensate　the　feature　map　of　the　current　frame　according　to　the　feature　maps　of　multiple　adjacent　frames.　False　negatives　and　false　positives　can　be　reduced　through　temporal　feature　fusion　in　video　object　detection.　In　addition,　multiscale　feature　maps　produced　by　the　feature　pyramid　network　can　detect　the　object　of　different　sizes,　and　the　multiscale　feature　map　fusion　can　enhance　the　semantic　information　of　the　low-level　feature　map,　which　improves　the　detection　ability　of　the　low-level　feature　map　for　small　objects.　©　2021,　Editorial　Office　of　Journal　of　Image　and　Graphics.　All　right　reserved.